COVID-19: why did a second wave occur even in regions hit hard by the first wave?

By Nic Lewis

 Introduction

Many people, myself included, thought that in the many regions where COVID-19 infections were consistently reducing during the summer, indicating that the applicable herd immunity threshold had apparently been crossed, it was unlikely that a major second wave would occur. This thinking has been proved wrong. In this article I give an explanation of why I think major second waves have happened.

Key points

  • The herd immunity threshold (HIT) depends positively on the basic reproduction number R0 and negatively on heterogeneity in susceptibility.
  • Since neither of the factors on which the HIT depends are fixed, the HIT is not fixed either.
  • R0 depends on biological, environmental and sociological factors; colder weather and the evolution of more transmissible strains likely both increase R0; more (less) cautious behaviour and social distancing / restrictions on mixing reduce (increase) R0.
  • Second waves were due primarily to changes in these factors increasing R0 and thus the HIT from below to above the existing level of population immunity.
  • Heterogeneity in susceptibility is partly biological, but social connectivity differences are key.
  • The effect of heterogeneity in susceptibility on the HIT can be represented by a single parameter λ.
  • λ will always exceed 1 (its level in a homogeneous population); pre-epidemic λ may be ~4. The higher λ is, the lower the HIT for any given R0.
  • The natural infection HIT is hence bound to be below the level of {1 – 1/R0} quoted by ‘experts’.
  • Government restrictions reduce λ as well as R0, so the HIT falls less than it would if λ were fixed.
  • The final size of an uncontrolled epidemic will substantially exceed the HIT, due to overshoot, so high reported seroprevalence levels can be consistent with a much lower HIT.

The herd immunity threshold (HIT) for a disease epidemic is the proportion of the population needing to have been infected, and thereby no longer susceptible to infection, before the rate of new infections starts to decline. The HIT depends both on the basic reproduction number for infections (R0) – the number of other people that at the start of an epidemic an infected person will on average infect – and the degree of heterogeneity in individuals’ likelihood of being infected (their susceptibility). That likelihood in turn depends on both their social connectivity and biological susceptibility to infection. Neither R0 nor the degree of heterogeneity in susceptibility is fixed in value, so the HIT is not fixed either.

Changes in population behaviour – whether arising from government interventions or in response to increasing disease incidence – affect both R0 and heterogeneity in susceptibility. In addition, R0 (which is proportional to how readily infection is on average transmitted between individuals) may vary seasonally, and change as the virus or other infectious organism mutates.

The resurgence of COVID-19 infections in a second wave after the summer ended is almost certainly due to some combination of the foregoing sociological and biological factors. It has been claimed that the influence of weather on its transmission is relatively minor,[1] and it has so far proved difficult to detect seasonality for COVID-19.[2] However, common colds caused by other coronaviruses are highly seasonal and I now think that it is reasonable to work on the basis that COVID-19 shares that behaviour.

I focus in this article on the mathematical dependence of the HIT to R0 and heterogeneity in susceptibility, and on the factors influencing those controlling variables. I also touch on difference between the HIT and the final size of an uncontrolled epidemic. I discuss in an appendix how, in my view, changes in the factors influencing R0 and heterogeneity in susceptibility likely shaped the evolution of the epidemic in western Europe

How the HIT varies with R0 and population heterogeneity

Table 1 illustrates how the herd immunity threshold varies with R0 and population heterogeneity in susceptibility to infection. The effect of such heterogeneity on transmission of infection and on the HIT can be represented by a single parameter λ, the heterogeneity factor (Tkachenko et al. 2020)[3], which is a function of population variability in both social connectivity and in biological susceptibility.[4] The reproduction number at any time, Rt, and the HIT are related as follows to R0 and λ:

Rt = R0 × Sλ

HIT = 1 – (1/R0)1/λ

where S is the proportion of the population that remains susceptible to infection. For a homogeneous population, these formulae reduce to the classical results Rt = R0 × S and HIT = 1 – 1/R0. With heterogeneity in susceptibility to infection, Rt falls more than pro rata to the susceptible proportion S decreases. Initially, Rt falls λ times as fast with S as in the homogeneous case.

Note that an epidemic takes some time to die out after the HIT is reached, since at that point many people will be infected and will go on to infect others, albeit at a declining rate. Therefore, the final size of the epidemic (FSE) – the attack rate (the ultimate proportion of the population that has been infected) – will exceed the HIT. The columns to the right of each HIT column show (in italics) the FSE if social and biological factors remain unchanged throughout the epidemic.[5] As shown in a previous article,[6] well timed short term restrictions to reduce transmission as the HIT is approached can prevent the FSE from significantly overshooting the HIT.

Table 1. Relationship of each of the herd immunity threshold (HIT) and the final size of the epidemic (FSE) with the basic reproduction number R0, at varying levels of heterogeneity factor λ that arises from heterogeneity in susceptibility (assumed gamma-distributed) across the population, from none (λ = 1) to an estimated normal level (λ = 4). The FSE values assume that the same R0 and λ value applied throughout the epidemic.

Since a person’s social connectivity, which reflects their average rate of contacts with others, equally affects their infectivity, variability in it has a more powerful effect than variability in biological susceptibility.[7] Note that heterogeneity in infectivity that is uncorrelated with susceptibility does not affect the overall progression of an established, large epidemic, although it may affect smaller scale features such as clustering of cases.

For a population that is homogeneous in both biological and social components of susceptibility, λ = 1 (pink columns). In that case, the ‘classical’ formula HIT = 1 – 1/ R0 is valid. This formula also applies to immunity gained through vaccination at random, since such vaccination – unlike natural disease progression – does not preferentially confer immunity on individuals who are more susceptible to infection (and also more likely to infect others).

Analyses of contact networks indicate that, in normal circumstances, the coefficient of variation (standard deviation / mean) for social connectivity in a population is about 1, while biological susceptibility is likely to have a coefficient of variation of about 1/3 or more (Tkachenko et al). Use of those figures implies that λ = 4 (green, rightmost columns).

The effect of government social distancing measures on R0 and the heterogeneity factor

It has been estimated that, prior to significant social distancing taking place, 80% to 90% of all transmission of infection is caused by circa 10% of infected individuals, often at superspreading events where a large number of people are present. When restrictions on gatherings, bars and other venues are introduced, non-household social mixing generally is reduced and superspreading opportunities fall even further, while household mixing will be little affected. The result will be a reduction in R0, but also reduced heterogeneity in social connectivity and hence λ. A further reduction in both these factors can be expected to occur when a lockdown (stay-at-home order) is introduced.

The effects of such government measures, for a range of resulting R0 values, are illustrated by the two middle sets of columns. These both assume the same 1/3  coefficient of variation for biological susceptibility, but a reduction in the coefficient of variation for social connectivity to 0.625, resulting in λ = 3 (yellow columns) or to 0.25, resulting in λ = 2 (salmon columns).

Even in the absence of legal restrictions being imposed, people can be expected to significantly change their behaviour when an epidemic involving severe disease takes hold. The resulting reduction in λ, for any given resulting reduction in R0, might however be less than under an enforced reduction in mixing, since more gregarious people may be less cautious and reduce their high social mixing proportionately less than more cautious, less gregarious people do – the opposite relationship to that arising from restrictions on gatherings, bars and other venues.

How a high seroprevalence level can arise even in the presence of substantial heterogeneity

It might be thought that a high attack rate is incompatible with significant population heterogeneity in susceptibility and hence a moderate HIT. An attack rate of 76% has been claimed for the city of Manaus.[8] However, the weighted measured seroprevalence on which that estimate was based was not from a random sample nor representative of the population,[9] and never exceeded 44%[10]. A random population survey found seroprevalence in Manaus to be only between one-quarter one-third the level claimed in the foregoing study, casting severe doubt on its claim.[11]

The first mentioned study also estimated that in or just after mid-March, near the start of the epidemic in Manaus, Rt – which at that point would not have been far short of R0 – was approximately 2.5, suggesting R0 was in the 2.6 to 2.8 range. The extent of physical distancing that they estimated applied then was moderate, similar to that near the end of the main epidemic. In a relatively poor city like Manaus with household and transport crowding it seems quite likely that in normal circumstances there is lower population heterogeneity in social connectivity than in a high income city, indicating an heterogeneity factor λ perhaps more like 3 than 4 (yellow not green columns). And under moderate social distancing the heterogeneity factor λ might be closer to 2 than 3. For an R0 of 2.6, λ = 2 implies an HIT of 38% but a final epidemic size (FSE) of 64%[12]. Even at λ = 3, the FSE would be 49% (with an HIT of 27%).[13]

To summarize, it seems doubtful that the attack rate in Manaus in fact exceeded 50% – it may have been no more than 20-25% – and an attack rate of 50% is fully compatible with the HIT being below 30%.


Appendix – Changes in R0 and population heterogeneity during the epidemic

The following discussion, which represents my semi-quantitative broad brush analysis of what has occurred, relates primarily to the progress of the epidemic in western Europe. However, it may also be somewhat applicable to the north east United States, where the epidemic took off only slightly later than in western Europe and where the seasonal variation in climate is also large.

In the initial stages of the first wave, which generally started in major cities, in early spring 2020, infections appear to have been doubling every three days or so prior to governments imposing restrictions or people becoming significantly more cautious. Depending on the assumed distribution of the generation interval (from one infection to those it directly leads to), that implies an R0 value of between 2 and 4.[14] I will assume a  middle of the range R0 value of 3 for illustrative purposes. That would imply a HIT of 67% for a homogeneous population, reducing to 24% for a population with the highest degree of heterogeneity illustrated in Table 1, which might be expected to apply before people started behaving more cautiously and mixing less.

When people started mixing less, voluntarily or by government fiat, R0 would have reduced, but as discussed above λ will also have fallen. The combined effect of these changes can be visualised as moving diagonally upwards and leftwards in Table 1, from the green columns to the yellow columns and then to the salmon columns. The resulting reduction in the HIT would therefore be somewhat smaller than that implied by the reduction in R0 alone.

By late spring or early summer the first wave had largely faded, and it generally continued to decline after restrictions on mixing were at least partially relaxed. As summer progressed, people’s behaviour unsurprisingly returned closer to pre-epidemic norms. I will assume for illustrative purposes that the yellow columns (λ = 3) were representative of that period. Since by midsummer the epidemic appears to have been declining even where only a minor first wave had occurred, it seems that R0 must generally have declined to 1 or below, so that population immunity levels would everywhere have exceeded the HIT (which is only positive if R0 > 1).

As autumn arrived, infections and then serious illness started to rise again, although where testing was increasing the rise may have been exaggerated. It follows that R0 must have risen again, resulting in the HIT increasing to above the level of population immunity. An obvious explanation for the rise in R0 is seasonally reduced sun and cooler weather, with more contact occurring indoors, where almost all COVID-19 transmission appears to take place. A major increase in mixing among young people as school and, particularly, university terms started likely also boosted R0 and the level of infections in the autumn; young adults have generally had the highest incidence rates during the second wave.[15] In some places the rise in infections appears to have occurred slightly earlier, perhaps as a result of holidaymakers returning infected from areas where COVID-19 was more prevalent.[16]

Initially it seemed that some large cities where a significant proportion of the population had been infected in the first wave might be spared, but in most cases the increase in R0 evidently became sufficiently large to raise the HIT to above the level of population immunity. As a result of increasing infections, government-imposed restrictions were generally increased, which as well as reducing R0 will also have reduced the heterogeneity factor λ. This can be visualised as a move diagonally upwards from the yellow columns to the salmon columns. Those actions appear typically to have pushed Rt down to about 1, or slightly lower, which in the presence of a reasonable degree of existing population immunity implies an R0 level significantly above 1. With reduced heterogeneity, the existing level of population immunity causes a lesser reduction in Rt, relative to R0, but Rt will still be a smaller fraction of R0 than the proportion of the population that remains susceptible to infection.

In the UK, and possibly various other countries, a new lineage (B.1.1.7) of the SARS-CoV-2 virus has now emerged[17] and grown faster than existing ones, as discussed in a previous article[18]. Since writing that article, some further data has provided less indirect evidence that B.1.1.7 is 25–50% more infectious than pre-existing variants.[19] On the other hand, recent data from the regions where B.1.1.7 has become dominant suggests that it may now be growing no faster than other variants.[20] It has been suggested that the fast growth in the regions where B.1.1.7 now dominates may have been at least partly due to it spreading there in schools.[21] However, making for illustrative purposes the assumption that B.1.1.7 is actually 25–50% more infectious, R0 will have been increasing, perhaps typically reaching somewhere in the range1.5 to 2.0 once B.1.1.7 becomes the dominant variant, if R0 was previously in the 1.2 to 1.4 range.

Tougher restrictions that have been introduced in a number of countries in response to infection rates increasing, whether due to the spread of the B.1.1.7 lineage, to cold winter weather or to greater mixing, will have reduced population heterogeneity in social connectivity further. In these circumstances,  is unclear whether existing levels of population immunity will suffice to prevent further growth of the B.1.1.7 lineage, or the rather similar one that has emerged in South African, even with severe restrictions being introduced. However, increased population immunity resulting from some combination of further spread of infections and vaccination programmes, the combination varying from one country and region to another,  should bring COVID-19 epidemics under control within the next few months.

 

Nicholas Lewis                                                                                  10 January 2021


[1]  “All pharmaceutical and non-pharmaceutical interventions are currently believed to have a stronger impact on transmission over space and time than any environmental driver.” Carlson CJ, Gomez AC, Bansal S, Ryan SJ. Misconceptions about weather and seasonality must not misguide COVID-19 response. Nature Communications. 2020 Aug 27;11(1):1-4. https://doi.org/10.1038/s41467-020-18150-z

[2]  Engelbrecht FA, Scholes RJ. Test for Covid-19 seasonality and the risk of second waves. One Health. 2020 Nov 29:100202.  https://doi.org/10.1016/j.onehlt.2020.100202

[3]  Tkachenko, A.V. et al.: Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19. medRxiv 29 July 2020 https://doi.org/10.1101/2020.07.26.20162420  They use the term ‘immunity factor’ for λ. Equations [11)],  [12] and [13] and intervening paragraph. I adopt their assumption that there is negligible correlation across the population between biological susceptibility to infection and either social connectivity or biological infectivity.

[4]  I make from here on the common assumption that a gamma distribution can well represent variation within the population in both social connectivity and biological susceptibility, on which basis λ = (1 + 2 × CVs2) × (1 + CVb2) where CVs and CVb are respectively the social and biological coefficients of variation (standard deviation / mean) for the population.

[5]  The FSE (1 – S) depends on the sum of the squared coefficients of variation η = CVs2 + CVb2 as well as on λ. It is given by the solution to the equation S = (1 + R0 η [1–Sλη]/[ λη])–1/η. See Tkachenko et al. equation [17].

[6]  https://www.nicholaslewis.org/when-does-government-intervention-make-sense-for-covid-19/

[7]  Variability in infectivity that is uncorrelated with susceptibility in the population has no overall effect in a sizeable epidemic.

[8]  Buss, Lewis F., et al. “Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic.” Science (2020).

[9]  It was a convenience sample, comprised entirely of blood donors.

[10] That maximum seroprevalence estimate was adjusted upwards to 52% to account for test for sensitivity and specificity. The attack rate estimate further assumed that antibodies would no longer be detectable in a proportion of previously infected individuals.

[11] Hallal, P.C. et al:SARS-CoV-2 antibody prevalence in Brazil: results from two successive nationwide serological household surveys. Lancet, 8(11), e1390-e1398,, September 2020 https://doi.org/10.1016/S2214-109X(20)30387-9

[12] Actually slightly lower, as the stricter social distancing measures in the middle part of the epidemic would have reduced the excess of the FSE over the HIT.

[13] If  R0 = 2.0, which is possible if a shorter estimate of the generation interval is used, the corresponding FSE sizes would be 52% or 38%, with the HIT being respectively 29% or 21%.

[14] Assuming a gamma distributed generation interval with a mean in the range 4 to 6.5 and a coefficient of variation between 0.37 and 0.74.

[15] Aleta A, Moreno Y. Age differential analysis of COVID-19 second wave in Europe reveals highest incidence among young adults. medRxiv. 13 November 2020. https://doi.org/10.1101/2020.11.11.20230177

[16] It is also possible that, notwithstanding a published finding to the contrary, the A20.EU1 variant that was brought back from Spain by people infected on holiday there may have been somewhat more infectious than existing variants.

[17] Other evidence that has now become available suggests that a similar variant arose in Italy prior to B.1.1.7 being detected in the UK.

[18] https://www.nicholaslewis.org/the-relative-infectivity-of-the-new-uk-variant-of-sars-cov-2/

[19] The observed 50–70% increase in weekly growth rate corresponds to roughly a 25–50% increase in infectivity (and hence in R0), assuming a generation interval with a 4–6 day mean and a reasonable CV, if R0 was previously not substantially above 1.

[20] https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/adhocs/12722estimatesofcovid19casesto02januaryforenglandregionsofenglandandbycasescompatiblewiththenewvariant

[21] Loftus (2021, Jan. 1). Neurath’s Speedboat: Did the new variant of COVID spread through schools? Retrieved from http://joshualoftus.com/posts/2021-01-01-did-the-new-variant-of-covid-spread-through-schools/

Originally posted here, where a pdf copy is also available

328 responses to “COVID-19: why did a second wave occur even in regions hit hard by the first wave?

  1. But did not happen where they use Ivermectin.

    • FYI –

      The Association of American Physicians and Surgeons posted on their website a paper/brochure advising treatments at home for Covid-19 patients (a step-by-step doctors’ plan).
      They recommend two treatments; one features Hydroxychloroquine and the other features Ivermectin.

      Click to access CovidPatientTreatmentGuide.pdf

      • do they give the statistical evidence that it “works”.?

      • “Do they give statistical evidence that it works”?

        Would they?

        If “they” can do all sorts of “studies” to prove that HCQ is dangerous and doesn’t work, and if they can publish a paper in the Lancet on HCQ (proving how terrible it is) that was so bad that it had to be retracted, then why would we believe that anyone would care to do honest studies on either HCQ or Ivermectin, both of which are relatively cheap and have been used safely for years? https://ahrp.org/how-a-false-hydroxychloroquine-narrative-was-created/

        The point seems to be to discredit any alternative therapies that would take glory, and profits, away from vaccines.

  2. William B Dawson Jr

    Wish someone could translate to common understandable words.

    • > This thinking has been proved wrong.

      I [Nic Lewis] was wrong.

      • Can’t seem to get this to post in the right place.

        ATTP’s reference shows that herd immunity is indeed properly used as I have used it. VTG’s insistence I’m wrong is not rational and contradicted by all the evidence.

        “The term “herd immunity” is widely used but carries a variety of meanings [1–7]. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as “indirect protection” or a “herd effect”). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.”

        VTG has nothing on IFR either other than word salads unsupported by any references or any science. 0.03% is easy to explain in a place like Africa where the average age is 20 or so.

        ATTP’s reference shows that herd immunity is indeed properly used as I have used it. VTG’s insistence I’m wrong is not rational and contradicted by all the evidence.

        “The term “herd immunity” is widely used but carries a variety of meanings [1–7]. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as “indirect protection” or a “herd effect”). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.”

        VTG has nothing on IFR either other than word salads unsupported by any references or any science. 0.03% is easy to explain in a place like Africa where the average age is 20 or so.

        VTG is part of the despicable public attack on Ioannidis. These attacks are personal, nasty, and political. Partisan hacks should be ashamed of themselves for attacking one of the lions of medical science.

    • Layman’s summary: Epidemeology is very uncertain and the problem of predicting the progress of any epidemic is ill-posed and highly sensitive to parameters and assumptions. Nic did a good job of showing this in this post.

      • Nic Lewis chose a set of model parameters which could fit then current data and claim herd immunity based on a particular locality.

        Other localities and indeed choices of parameters could not support this claim, and as expected by almost the entire epidemiological community it has indeed proved false.

        In the meantime, right wing media outlets used this and other similar analyses to push a false and damaging claim that the pandemic could be ended through infection aquired herd immunity.

        How many lives have been lost as a result is a matter for speculation.

        Parallels with the climate change public debate are notable.

      • What a silly nonscientific comment. There has been no proof that herd immunity was not achieved in many places this summer and then R increased because of winter and a more contageous strain and HIT went up too. HIT can vary a lot as the current post shows.

        It’s the simplest explanation of the common pattern of most European countries and many US states of an initial wave which then abated in the summer (in many places going down to negligible deaths) and then a strong second wave. For example California and New York have had increasingly severe lockdowns and show this patter with California in a very strong growth phase right now.

        In any case, there are by now many many papers showing that strong mitigation measures don’t make a significant difference anyway. I cited a new one below. One could just as easily argue that strong mitigation has caused more excess deaths than it has prevented.

      • dpy,

        Thank you for your technical comment. The mathematic treatment is compelling.

        In a more serious note, your unscientific attempt to redefine herd immunity is noted.

        The Ionnadis paper you cited on suppression is as bad an attempt to critique virus suppression as his repeated failed attempts to deny reality on IFR. That’s a high bar! I note even you seem to have given up defending his IFR position. There’s a lesson for you there if you choose to find it.

      • Another vacuous response. Herd immunity is reached when R is less than 1 and infections trend toward zero. It varies a lot depending on many things. There are large numbers of papers digging into the details. Why do you bother with such blatantly political attacks? If you think you are convincing anyone you are wrong.

        You have no credibility in judging any science statistical result. Nic Lewis is a top flight professional stastician,

      • Nic Lewis was wrong.

        Herd immunity was not reached in Sweden.

        Nobody else defines herd immunity as reducing cases whilst restrictions are still in place. It’s a ludicrous attempt at justifying a failed hypothesis. Cases rose throughout Europe during summer once restrictions were lifted.

        Ionnadis was wrong about IFR and is now putting forward a risible proposition that South Korea and Sweden had equivalent responses(!)

        There’s nothing political here except those promoting these failed ideas. Like the promoters of Great Barrington Declaration, a document sponsored by a libertarian think tank, lest we forget.

        Learn your lessons: cherry picking science based on your politics leads to failure. Go apply it to climate change.

      • A quick google search shows how completely wrong and simple minded about science you are. I found one that is for laymen from Nature:

        “Reaching herd immunity depends in part on what’s happening in the population. Calculations of the threshold are very sensitive to the values of R, Kwok says. In June, he and his colleagues published a letter to the editor in the Journal of Infection that demonstrates this4. Kwok and his team estimated the Rt in more than 30 countries, using data on the daily number of new COVID-19 cases from March. They then used these values to calculate a threshold for herd immunity in each country’s population. The numbers ranged from as high as 85% in Bahrain, with its then-Rt of 6.64, to as low as 5.66% in Kuwait, where the Rt was 1.06. Kuwait’s low numbers reflected the fact that it was putting in place lots of measures to control the virus, such as establishing local curfews and banning commercial flights from many countries. If the country stopped those measures, Kwok says, the herd-immunity threshold would go up.”

        Ioannidis’ peer reviewed paper on this subject gives a wide range of IFR’s based on seroprevalence studies from 0.03% to 0.5%. It is simple minded and unscientific to try to find a single number. For a disease like this that is strongly age related, the profile of the population will give a wide range of numbers. There are by now a multitude of papers and studies that show that in healthy populations under 70 (for example blood donors) the IFR is in the 0.07% range. For the elderly and ill, its much higher. But they have a very high mortality rate in any case.

        You are the politically motivated activist here. The scientists who you slander are vastly more honest than you. And they actually know statistics and science.

        I personally don’t even know why you persist in this concensus enforcement activity as you will convince no one with your totally unsupported wrong assertions.

      • dpy,

        drop the victimhood? Pointing out you’re wrong isn’t enforcing anything. Relying, as you consistently do, on fringe science which aligns with your politics is a recipe for being consistently wrong.

        Ionaddis IFR numbers are, and have been throughout, an outlier. 0.03%!

        The interpretation of herd immunity you copy clearly is not compatible with no second wave hypothesis, so if used needs to be clearly in that context.

      • FWIW, the herd immunity threshold is typically defined in terms of R0. If we implement interventions that change our behaviour and, consequently, change the effective R number and limit the spread of the infection, the herd immunity threshold is still defined by R0, not by the effective R. The latter would only be the case if the interventions were essentially permanent and, hence, had changed R0. For example, this paper.

        https://academic.oup.com/cid/article/52/7/911/299077

      • David –

        > “Reaching herd immunity depends in part on what’s happening in the population.

        Imagine a scenario where a boat with 1000 people aboard get stranded on a desert isle.

        1 has COVID at the time of the shipwreck and then infects three others and then each of those 3 others infect two others after that. So a total of 10 have been infected.

        So then everyone isolates and 2 of those infected died and the rest recovered and developed antibodies and immunity and there’s no more virus on the island.

        So, according to your thinking, 0.1% of the population on the island has been infected and the island population has reached herd immunity.

        Basically, you’re equating “herd immunity” with infection level. You could call any prevalence of infection “herd immunity” depending on the conditions.

        Nic stated, that Stockholm had (likely, but with high confidence) reached “herd immunity,” specifically because he had identified the people in Stockholm as engaging in near normal-level behaviors. He has re-stated such in this thread. If you have reached “herd immunity” in near-normal level behaviors, changes in behavior (presumably because of season) would not result in the subsequent level of growth in infection rate that they’ve seen in Sweden since the date when Nic said they’d reached “herd immunity.” Well, unless those changes in behaviors included something like going around and deliberately infecting people

        For the term “herd immunity” to have some meaning it needs to mean more than “any level of prevalence of infection I want it to mean no matter the context.” To be meaningful, it needs to have some implication w/r/t the level of confidence people can feel that they won’t likely get infected under normal (or near-normal) conditions (with the understanding of course, that with overshoot there’s still some level of potential to get infected).

        Referring to an out of context “definition” is a weak rhetorical device.

      • ATTP’s reference shows that herd immunity is indeed properly used as I have used it. VTG’s insistence I’m wrong is not rational and contradicted by all the evidence.

        “The term “herd immunity” is widely used but carries a variety of meanings [1–7]. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as “indirect protection” or a “herd effect”). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.”

        VTG has nothing on IFR either other than word salads unsupported by any references or any science. 0.03% is easy to explain in a place like Africa where the average age is 20 or so.

        VTG is part of the despicable public attack on Ioannidis. These attacks are personal, nasty, and political. Partisan hacks should be ashamed of themselves for attacking one of the lions of medical science.

      • Posting in the correct place.

        ATTP’s reference shows that herd immunity is indeed properly used as I have used it. VTG’s insistence I’m wrong is not rational and contradicted by all the evidence.

        “The term “herd immunity” is widely used but carries a variety of meanings [1–7]. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as “indirect protection” or a “herd effect”). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.”

        VTG has nothing on IFR either other than word salads unsupported by any references or any science. 0.03% is easy to explain in a place like Africa where the average age is 20 or so.

        VTG is part of the despicable public attack on Ioannidis. These attacks are personal, nasty, and political. Partisan hacks should be ashamed of themselves for attacking one of the lions of medical science.

      • dpy,

        you’re hilarious, honestly.

        Where to start.

        IFR mainstream estimates:

        we estimate the overall IFR in a typical low-income country, with a population structure skewed towards younger individuals, to be 0.23% (0.14-0.42 95% prediction interval range). In contrast, in a typical high income country, with a greater concentration of elderly individuals, we estimate the overall IFR to be 1.15% (0.78-1.79 95% prediction interval range).

        https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-34-ifr/

        Herd immunity: even when presented with a reference you claim it says the opposite of what it actually does.

        “Despicable” attacks on Ionaddis is laughable. The guy has published endless dubious studies throughout the pandemic and has been proved wrong again, and again and again. He’s zero credibility left in the field but is the darling of the right wing media. That you find pointing this out “despicable” is a measure of how indefensible your position is.

        Some academic critique here. It’s not comfortable reading. For instance on that infamous Santa Clara study:

        The sampling method, the statistical tests, the sensitivity/specificity of the tests, are all reported to be seriously flawed

        Ouch.

        Click to access 2012.12400.pdf

      • Joshua,

        indeed, dpy’s definiton of herd immunity means that anywhere in the world where cases are dropping has reached herd immunity.

      • Summarizing: As proven by the citations I was right about the term herd immunity. VTG was wrong. And it is such an obvious error on his part that a minute of research would show it is an error. That indicates something rather dark.

        There is by now a vast literature on IFR for covid-19. Values are all over the place and that’s the expected result for this disease. Insisting on a single number is literal minded and wrong too.

        The controversy you cite is over the Santa Clara paper. By now, its largely irrelevant to the science as there are a huge number of seroprevalence studies, many of them in the same ball park as the Santa Clara paper. Los Angeles and Miami Dade to name two. Ioannidis has at least one paper doing a meta-analysis of 50 of these studies.

        Your vague pseudoscience attack on Ioannidis’ work on IFR is also wrong especially as you obviously haven’t read any of the papers. Again, it is pretty dark stuff.

        VTG wrong on all counts. That happens when the scientifically uninformed activist tries to comment on science.

      • For the medically illiterate who might be frightened by media headlines:

        Covid-19 is not very dangerous for healthy individuals under the age of 70. It is dangerous for those over 75 or those who are seriously ill. And yes for the uninformed, metabolic syndrome is a serious illness. It dramatically increases the risk of a heart attack for example. I did some calculations using the MESA study risk calculator. For an 80 year old man, entering typical values for metabolic syndrome resulted in a 3 fold increase in heart attack risk over entering normal values. If you smoke in addition, your chances of a heart attack are very high. Any infection could easily push you over the edge.

        After along research effort into the medical literature, I believe that the most important public health priority is to get people to exercise, lose weight, and stop smoking.

      • VTG, I quoted the entire abstract of ATTP’s reference in a comment above. It says my definition is widely used. My reference shows an instance of scientists using it the way I did.

        That you can’t admit you are wrong on such an obvious point shows something dark.

      • Let’s all remember what Ioannidis says about denialists and science:

        “The anti-vaccine movement and climate emergency deniers are already drawing ammunition from the reversals of opinion and policy during the pandemic.”
        https://www.bmj.com/content/371/bmj.m4048.full

        “Many fields lack the high reproducibility standards that are already used in fields such as air pollution and climate change.
        […]
        It is a scandal that the response of governments to climate change and pollution has not been more decisive.”

        https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5933781/

        Ioannidis saying that anthropogenic climate change on same level of certainty as smoking killing people:
        17:17 to 18:22 of:
        http://rationallyspeakingpodcast.org/show/rs-174-john-ioannidis-on-what-happened-to-evidence-based-med.html

        Yet right-wing contrarians act like Ioannidis is on their side. Priceless.

      • Dpy,

        The abstract doesn’t support you, and neither does the paper.

        The darkness is all yours.

      • Quoting the abstract:

        “Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection.”

        That’s exactly how I used the term. You really are entering dishonest denial territory here.

      • For those whose reading comprehension is very low, I quote again from the abstract.

        “Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection.”

        That’s exactly how I used the term. You are obviously wrong VTG.

      • David –

        It’s certainly possible that anyone could use the term “herd immunity” to mean “the degree of prevalence of infection in a given community at a given time.”

        In the current context, that seems to me like a rather meaningless definition. And defining some arbitrary “herd immunity threshold” as some point at which the rate of infections drops, irrespective of context such as interventions in place, or whether it might rise again dramatically without significant changes in the mitigating circumstances as happened in Sweden, is similarly meaningless.

        You might as well just say “the level of the population that is infected.”

        But it’s clear what Nic meant when he said that Srockholm had hit a “herd immunity threshold”
        8 months ago – as we have seen in how Rand Paul referenced Nic’s conclusions about his toy model.

        And Nic was wrong. That doesn’t diminish his skills as a statistician. But it also doesn’t diminish the errors he made in applying his skills to a particular context, errors that may not been because of technical flaws, but flaws in his scientific process of application.

        The same would Ioannidis’ errors, such as when he went on a national publicity campaign and wrongly suggested we’d top out at perhaps 40,000 deaths in the US, or confidently said that COVID is essentially like the seasonal flu, or when in the Santa Clara study they employed statistical methodology that has been severely criticized by expert statisticians, or when they extrapolated from unrepresentative concenience sampling, or when they promised potential study participants “immunity passports” if they were tested for antibodies when there was a good chance that they’d return a false positive, etc.

        And neither does your appeal to Nic’s and Ioannidis’s authority take away your laughable over-evaluation of your own competence for assessing the pandemic, such as that led you to argue that it was unrealistic to think that vaccines would meaningfully mitigate the reach of COVID, or that led you to declare that the pandemic was over in the US in the summer, or to declare that the summer surge wouldn’t result in a spike in deaths, only a spike in infections among young people or a merely spike in positive test results that is a function of a spoke in tests.

        But I do appreciate your sense of irony, expressed by your posting here at Climate Etc, with your constant appeals to authority, and your constant assertions that asking for people to allow greater respect for uncertainties is tantamount to vocusous personal attacks that have no merit.

        So thanks. Each and every time you post a comment you’re helping to strengthen arguments, and I wouldn’t want you to think I don’t appreciate it.

      • Josh, I don’t know why you post such long comments that are largely meaningless.

        Using a commonly used (by scientists) definition of herd immunity, there is no evidence Sweden did not reach herd immunity for summer time. You provided no evidence of any kind and just asserted something. Coming from a person who has demonstrated a deep ignorance of science and mathematics, I doubt anyone will bother to read what you just wasted a significant amount of life energy typing in. Nic is vastly more credible.

        Your criticism of Ioannidis is likewise simply a recitation of what others said and your own completely unsupported assertions. You have no way to verify who is right. Ioannidis has published hundreds of excellent contributions and has the most cited medical paper of the last 20 years. He’s a lion of the field. You are an anonymous nonscientist internet persona who has absolutely no qualifications or expertise to say anything about science.

      • David –

        > Using a commonly used (by scientists) definition of herd immunity,

        People use different definitions if terms and words in different contexts.

        Yes, “herd immunity” could theoretically mean effectively “the % of people in a given community who are immune.”

        But that meaning is useless in this context.
        And more to the point it is clearly not what Nic meant when he was talking about “herd immunity” in Sweden. Also, by “herd immunity threshold” he didn’t just mean the point at which, in some generic sense, there was a drop in the infection rate.

        That is made clear by his comments after he said they’d reached a “herd immunity threshold” and then he looked at a flat rate of infections for period and said it confirmed his earlier asssertion of having crossed that point.

        IOW, it’s clear his asu

      • Repeating a falsehood doesn’t make it true. You still have presented nothing supporting your nonsense assertions about Nic being wrong about herd immunity in Sweden. There was evidence they reached herd immunity, I.e., R<1.

      • OK, dpy, so now you’re clear.

        You’re claiming that anywhere R<1, he'd immunity has been reached.

        So, in the UK, we reached herd immunity in May.

        By August, we'd lost herd immunity.

        In November, we gained it again.

        December, lost it again.

        January, we've got herd immunity again.

        Glad that's clear.

      • It’s certainly the definition used by Kwok in the Nature piece I quoted from.

        “Though an important paper by Fox et al in 1971 [1] argued that emphasis on simple thresholds was not appropriate for public health, because of the importance of population heterogeneity, assumptions of homogeneous mixing and simple thresholds have persisted. ”

        Like many very simple models of complex systems, epidemiological models assuming a homogeneous dynamic are not very useful in explaining an ill-posed system like the course of any epidemic. Insistence on simple minded verbal formulations is not helpful for understanding and is not science.

        So yes, epidemiology is a field fraught with huge uncertainties. Sniping at respected scientists by anonymous nonscientists who don’t even know the basics is not helpful to anyone. You know who you are.

      • Insisting on using R0 to define HIT seems overly simplistic and not very useful. I don’t even think R0 is well defined or useful for modeling an epidemic. R obviously changes over time. It’s obviously seasonal. Viruses typically mutate and new strains will be more or less contagious. I’ve seen some evidence that in Britain there is a new strain that is much more contagious than the older strains. It seems reasonable to say that a new strain would change R0. In fact I’m not sure that R0 is really a very meaningful concept. Is it the R at the start of the epidemic or at some vaguely defined point in the epidemic. If its R at the beginning, its not a useful concept because then the HIT would depend strongly on what season the epidemic started.

      • Its not an insult to truthfully point out that someone has no qualifications to be taken seriously on scientific questions. It’s childish to comment here without even doing some research on the topic at hand.

      • David –

        Here is what Nic wrote:

        > I also projected, based on their declining trend, that total COVID-19 deaths would likely only be about 6,400. Subsequent developments support those conclusions. Swedish COVID-19 deaths have continued to decline, notwithstanding a return to more travel and less social distancing, and are now down to 10 to 15 a day.

        Based on HIS DEFINITION of “herd immunity,” Nic protected at total of 6,400 death. Today their reporting surpassed 10,000 dead and it will clearly go higher. I’ll guess that his projections might be off by as much as 200% by the time they get to herd immunity though used if vaccines (that you said wouldn’t matter) by the time all is said and done.

        Once again, his projections were based in HIS DEFINITION of “herd immunity.” this there is no question that his modeling if “herd immunity” was wrong as was his assertion that Sweden had crossed a “herd immunity threshold” as HE CONCEIVED THE ISSUES…

        He also said it was likely that NO COUNTRY would surpass a population fatality rate of 0.085%. There are some 30 countries that have passed that mark – some by more than double. Obviously, his erroneous thinking in that regard was to some large degree based on his misunderstanding of the mechanisms of “herd immunity.”

        There no crime in his having been wrong, but your insistence that he wasn’t wrong and that I’m incapable of pointing out his error because I’m not a scientist is just silly.

        You should stop with that.

      • Josh, I’m simply not going to read your comments as they are very long, totally redundant and completely free of any evidence or documentation. We’ve been over your distortions before many times. That you need to gain attention by such personal attacks is a sad commentary. Don’t you have a day job or relatives and friends to interact with?

        No one is right 100% of the time. That’s especially true in your case. Why you can’t just agree to disagree is very childish

      • Josh you are such an ankle biter. Nic’s projection of deaths was wrong. Winter came, R went up and there were new strains. His statement about herd immunity was in my view correct at the time it was made. Epidemiology is a field with vast uncertainties so most projections will be wrong.

        Do you have anything interesting to say that might help people learn? So far, your ratio of words to actually relevant science is almost infinite. Instead you spend time obsessing over minor points in an otherwise excellent body of work by some very competent people.

        If this is the only way you can get people to pay attention to you, you are pathetic. You must spend virtually all your time on the internet picking at people vastly more credible than you are. On your tombstone will be written: “Josh: who burned his chest when he fell asleep with his laptop open and obfuscated everything he ever participated in”

      • Yes Josh, I’m glad to see you admit you are childish.

        What I have said here is true about herd immunity. I provided supporting evidence. What you have said is not helpful to anyone trying to understand anything. It’s simply you trying to tear down and call into question the credibility of people who know vastly more than you do.

        If you have any friends ask one of them to read some of your comments. You will be amazed that most will agree with my impression.

      • DPY,

        ATTP’s reference shows that herd immunity is indeed properly used as I have used it.

        No, it doesn’t really. It goes on to show that the herd immunity threshold is typically defined in terms of the basic reproduction number (R0) not the effective reproduction number.

      • So then ATTP you disagree with the abstract of your reference and Kwok as quoted in the Nature piece.

        “Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection” is not ambiguous.

        I posted below some reasons why R0 is pretty much useless. Just as an example because of seasonality R0 will be a strong function of the season the outbreak starts. R0 is not like the universal gas constant.

  3. …or some of those states that had a “very high number of cases” actually _didn’t_.

    A grossly high false positive rate would seem to show a high level of infections in the early stages (without actual cases), but still allow for a high positive rate in later waves – because the population didn’t actually reach the herd immunity threshold.

    Likewise, if a population had a very high “retest” rate, with a significant number of people being counted as positive for each test (once a day in some places, according to some reports), you’d be well below the HIT even if you had a giant pile of true positive tests.

  4. Everett F Sargent

    “The FSE values assume that the same R0 and λ value applied throughout the epidemic.”

    Which is a extremely supremely BAD assumption! /:

    • No doubt in practice, but this is for the case of an uncontrolled epidemic.
      It wouldn’t make much difference if R0 and λ were different in the relatively early stages of the epidemic from thereafter, and for an uncontrolled epidemic it isn’t obvious that it is a bad assumption after that point.

      • Everett F Sargent

        “uncontrolled epidemic”

        Unfortunately, and in practice, that would never happen (in a modern society with near instant global communications and existing codependent technologies (our health care systems and R&D thereof)). And in all countries concerned, that did not happen.

        I’ve used the Groundhog Day analogy three times now. Every day of the pandemic all previous days are forgotten wrt the current day, Noone notice that a pandemic has been going on but do notice missing people and they conclude that those missing people are somewhere else on vacation.

        Anyone noticing a pandemic would employ some methods of protection in an attempt to minimize (or reduce) further spread, infections and deaths. I am pretty sure that is what has happened to date.

      • Matthew R Marler

        niclewis: It wouldn’t make much difference if R0 and λ were different in the relatively early stages of the epidemic from thereafter, and for an uncontrolled epidemic it isn’t obvious that it is a bad assumption after that point.

        With all due respect to your efforts in these essays, those are unsupportable statements. A model and its assumptions have to be shown to be accurate before any consequences are believed or depended on. Given that models are simplifications, the user of the model has the burden of proof that the simplifications do not cause important errors. These points have been made about GCMs and other models.

  5. My simple theory is that, since there were reports of antibodies being shorter-lived as well as mutation, makes it much like seasonal Influenza. I think herd immunity is achieved each season, whether by immunization or affliction.

  6. Nic –

    Over a period of months, you continually downplayed accumulating and clear evidence that you were wrong in your confident predictions about populations crossing a HIT some 8 months ago.

    You tried to say that rising infections were a “blip.” You tried to shift to an excuse that deaths weren’t rising in parallel with infections (by wrongly not allowing sufficient time for an established pattern of lags to equalize).

    You basically dismissed the possibility that the drop in infection rates in Sweden were due to seasonality when you argued that infections had dropped there contemporaneous with effectively normal behaviors. If a sufficient % of the population has been infected and recovered or died under normal behaviors, then their “herd immunity” will not surge again in the magnitude we have seen (unless the virus mutates or immunity from infection wanes). On that basis you strongly argued that other countries should follow Sweden’s COVID policies.

    You even went on to basically make up a theory as to why populations had reached a “herd immunity threshold” despite relatively low %’s of seropositivity from testing (because of T-cell reactivity that you inferred caused “T-cell immunity”)

    I’ll remind you, once again, that along with Willis you looked at some lines on a computer screen and tried to reverse engineer about the obviously complex realities in how disease spreads in real context. Because of your confidence and lack of respect for uncertainties in that regard, you agreed with Willis that COVID fatality would likely top off at 0.085% of the population. There are near 30 countries that have passed that population fatality percentage and some countries are close to double that figure.

    It seems rather clear that all along the problem was a lack of sufficient allowance on your part in your calculations for the effects of uncertainties.

    Now you want to focus on seasonality. If guess because you think that will give you some way of having been less wrong. But I would suggest that you’re making the same mistake again. You aren’t accounting for enough of the uncertainties related to seasonality, either.

  7. > However, increased population immunity resulting from some combination of further spread of infections and vaccination programmes, the combination varying from one country and region to another, should bring COVID-19 epidemics under control within the next few months.

    And here you ignore another uncertainty: The possibility that with a greater number of infections the greater the possibility of mutations that are vaccine resistant.

    With NPIs, we increase the chance of lowering rate of infections so as to allow for enough time to reach herd immunity through vaccinations as opposed to infections (or to least shift the % of total immunity that would be attributable to vaccinations as opposed to infections).

    Perhaps a problem here is that it seems each time you underestimate the uncertainties related to herd immunity, it’s in one direction only. That could just be coincidence, but at some point it might be worthwhile for you to ask yourself the question as to whether your political biases might be influencing your analytical processes.

    • I don’t see that Nic has argued against NPI’s? Though it’s becoming better understood that more vs. less restrict NPI’s make little to no difference in spread[1].

      As a passive observer, your case is lessened when you make ad hominem attacks. I’d suggest eliminating that approach from your strategy if you’d like to more effectively persuade.

      [1] https://onlinelibrary.wiley.com/doi/10.1111/eci.13484

      • Variant –

        > As a passive observer, your case is lessened when you make ad hominem attacks

        Fair enough. Point taken.

        If you look back over Nic’s series of comments you will see a clear advocacy against NPIs as a way to reduce fatality from COVID.

        I’ll look at your link, thanks. But I do doubt that one link will prove conclusive as you suggest. From what I’ve seen there’s a lot of uncertainty regarding the efficacy (or lack thereof) of NPIs that can’t be resolved until we have much better data.

        Given the uncertainties, before immunity though vaccination that was at least understandable even it proved to be wrong.

      • Oops. Last paragraph should be…

        Given the uncertainties, before immunity though vaccination was a realistic possibility the fast herd immunity as opposed to NPIs was at least an understandable option even if it proved to be wrong in the end. Perhaps it was true that in the end, the same number would get infected either way despite a slowing effect from NPIs. Pending your link proving conclusive, I’d say that it’s just common sense that NPIs will lower the rate of infections. If you want to argue that they differentially cause more deaths from knock-on effects, I’d love to read it. I’ve yet to se anyone actually take that on as opposed to just conflating correlation with causation and making unsupported counterfactual assumptions about what would be if things were different.

    • I’ll just note here that Josh’s long and boring litany is not supported by any citations or evidence. It’s just his impressions which in the past have proven to be often wrong.

      Nic’s posts are generally informative and interesting.

      I see no other viable explanation for the summer near extinction of the epidemic in many areas except herd immunity. Many epidemiologists were fearful that a second winter wave would happen and it did. Variant’s paper is interesting. My comparison of US states also suggests that very strong restrictions have little additional benefit over voluntary actions. The very strong growth in the last week in California is not explainable otherwise.

      • Josh, You are just so wrong and biased. The data showed that in fact it was under control this summer in many if not most places.

        You have a pattern of obsession with scientists and trying to tell them how to think and how they are biased. That’s called mind reading and its unethical for any professional. Why would anyone with scientific credentials even take the time to read your truly vacuous statements?

    • “And here you ignore another uncertainty: The possibility that with a greater number of infections the greater the possibility of mutations that are vaccine resistant.”

      The mutations that are worrisome appear to have arisen not from an increased number of infections, but rather from well meaning (but it seems highly dangerous) health system actions that inadvertently acted rather like gain of function-like experiments carried out in non-biosafe hospitals, in the UK, Italy and quite likely South Africa.

      • Thank you for your work, Nick. It’s always a pleasure to read it.

        If possible, could you please post a link to an article that deal with:
        “The mutations that are worrisome appear to have arisen not from an increased number of infections, but rather from well meaning (but it seems highly dangerous) health system actions that inadvertently acted rather like gain of function-like experiments carried out in non-biosafe hospitals, in the UK, Italy and quite likely South Africa.”

      • > This variant is a wake up call that we should try to really decrease transmission of Sars-Cov-2,” he said. “It is clear that if you leave it circulating, the virus has the ability to outsmart us and become better at transmission and evasion of the antibody response.”

        https://www.telegraph.co.uk/global-health/science-and-disease/third-concerning-coronavirus-variant-should-wake-call-world/

  8. Pingback: COVID-19: why did a second wave occur even in regions hit hard by the first wave? – Climate- Science.press

  9. In terms of the second wave in some areas, I wonder what role the additional six or eight months of aging and deteriorating health has on the susceptibility of already vulnerable people. It is obviously not simple to evaluate given the plethora of restrictions and relaxations, seasonality in one form or another, etc. In Saskatchewan, the death rate was linear but very low for months, but after rising in November is now linear again, but steeper. Care homes and northern communities seem to be bearing the brunt (although the toll is not high by any means). This trend is not all that different from Sweden over the past couple of months it seems. We just never had a first wave.

  10. The most successful predictions regarding this disease have been those that assumed it would behave like every other seasonal respiratory infection.

    • Except ALL other respiratory infections have dropped to near zero. Year over year comparisons will tell us a lot about public health.

      • You two amuse me. I was quite confident that there would be a resurgence in fall and winter. No one could predict with any confidence the size of that resurgence. There is just as much value in understanding what can’t be predicted with any accuracy as what can be.

      • And a reduction of other seasonal infections is normal during a pandemic.

    • There’s plenty of evidence that shows the picture is complicated:

      First identified in Wuhan, China, in December 2019, a novel coronavirus (SARS-CoV-2) has affected over 16,800,000 people worldwide as of July 29, 2020 and was declared a pandemic by the World Health Organization on March 11, 2020. Influenza studies have shown that influenza viruses survive longer on surfaces or in droplets in cold and dry air, thus increasing the likelihood of subsequent transmission. A similar hypothesis has been postulated for the transmission of COVID-19, the disease caused by SARS-CoV-2. It is important to propose methodologies to understand the effects of environmental factors on this ongoing outbreak to support decision-making pertaining to disease control. Here, we examine the spatial variability of the basic reproductive numbers of COVID-19 across provinces and cities in China and show that environmental variables alone cannot explain this variability. Our findings suggest that changes in weather (i.e., increase of temperature and humidity as spring and summer months arrive in the Northern Hemisphere) will not necessarily lead to declines in case counts without the implementation of drastic public health interventions.

      https://www.nature.com/articles/s41598-020-74089-7

      –snip–
      However, in comparison to other respiratory viruses such as the flu, COVID-19 has a higher rate of transmission (R0), at least partly due to circulation in a largely immunologically naïve population. This means that unlike the flu and other respiratory viruses, the factors governing seasonality of viruses cannot yet halt the spread of COVID-19 in the summer months. But, once herd immunity is attained through natural infections and vaccinations, the R0 should drop substantially, making the virus more susceptible to seasonal factors.

      https://healthcare-in-europe.com/en/news/covid-19-will-probably-become-seasonal-but.html

    • Joe - the non epidemiologist

      doubgadgero comment – “The most successful predictions regarding this disease have been those that assumed it would behave like every other seasonal respiratory infection.”

      To a large extent, covid has behaved similarly to prior seasonal respiratory infections. The timing of the surges have followed somewhat closely with the Hopes Simpson analysis (from 1990’s) with the dividing line with covid being approximately the 33 latitude instead of 30 latitude. The other notable exceptions being Canada, Norway and finland, all of which have much harder lockdowns and substantially reduced cross border travel.

  11. https://www.bmj.com/content/370/bmj.m3563

    I don’t know Nic if you have seen this. It seems to indicate that there is cross over T cell reactivity fo covid19 in many places. So far, its not really quantified well.

  12. Did Sweden, which apparently did not use draconian government controls, also suffer a severe 2nd wave?

    Am not a statistical or medical expert, but the general thought: better to take the damage early or drag it out? Do you ultimately end up at the similar medical and economic endpoints? These are more pragmatic queries…. and yes I am an engineer.

    I have not formed a firm conclusion, one way or the other. My hunch is that the law-of-unintended consequences rears it’s ugly head when governments think they can control outcomes that involve a vast armadas of variables and unknowns. Aim for the middle and hope for the best might be a better approach.

    • Swedish data takes up to a month to be finalized. But yes, it appears that they are having a second wave almost as strong as the first.

      • “almost as strong”

        Swedish deaths are above 1st wave and still rising.

      • when you look at morality that s not clear..
        https://www.euromomo.eu/graphs-and-maps/

      • The plain fact is that Sweden, which didn’t have the huge infringements on rights that most of the world had, fared just about as well as the rest of the western world. They did this without the huge harms to the social fabric and businesses that other western countries inflicted on their populations.

        All in all, was it really worth it? NY State, land of draconian measures, has suffered about the worst in the US; Florida, without such measures and with many more older people, is doing much better.

      • If you compare Sweden to the countries which are the most similar, and which have structural elements in terms of health outcomes predictors that would suggest similar results, it has done very poorly.

        It’s a complicated process. Isolating the predictive variables is complicated. But generally if you’re trying to assess the efficacy of interventions you’d want to compare situations where you’ve controlled for a any confounds as possible. Therefore, comparing to the other Nordic countries, rather than “the rest of the western world, ” is probably more instructive. Just as you wouldn’t compare to South Korea or Taiwan to assess outcomes in Sweden.

        Why do you think Sweden has fared so poorly compared to its Nordic neighbors?

        And btwz do you think that citizens of its Nordic neighbirs generally feel that they’ve had their rights trampled on relative to Sweden?

        What evidence would you use to assess
        disruptions to social fabric and businesses in Sweden relative to their Nordic neighbors?

        Florida is not doing “much better” than NY in terms of cases per capita. In fact, it’s doing worse. Considering the improvements in our understandjmg of how to treat people ill with covid and a number of other factors, and the timing of the surges in NY as opposed to Florida, comparing outcomes in those two states gets complicated. Consider that Florida, if you look at population density combined with population distribution (Iow, compare pop density in NY and Miami Dade), as well as the impact of seasonality, has important balancing factors that in contrast to median age, cthat work to the advantage of Florida with respect to expected outcomes.

    • Related to herd immunity, IOW rate of infections (as opposed to deaths), their second wave is considerably larger than their first. Although ascertainment may also be higher. And their testing rate may be higher. But their fatality rate may well be lower (maybe they’ve gotten at least somewhat better at keeping COVID out of senior congregate living and aren’t just leaving infected older people to die as much as they did during the first wave).

      IOW, When someone who has no idea what he’s talking about says “it appears that they are having a second wave almost as strong as the first.” you should prolly assume he doesn’t know what he’s talking about.

  13. Nic – take a look at our cases here in the low deserts of Arizona. Most of our population is at low altitudes and fairly low latitude. This means that right now is an optimal time to be outside rather than inside. The summer is the time we stay indoors.

    And yet, we had three peaks.

    The first was in the spring, was met with relatively strong mitigations, and was not very large.

    The second was in mid-summer (early July), when our high temperatures were way over 100F and our lows over 85F. And yet, it was a much higher peak, to the extent the it made world headlines.

    It went away – perhaps due to stronger mitigations put in place at state and also local levels than existed during its rise.

    Now we are in our winter peak, and we are at world headline levels again. Our 7 day average is 140 cases per day per 100,000. We still have the same mitigations that went into place just before the summer peak. A few of our schools are open.

    A couple of factors might affect this, but it’s hard to explain our current very high level (and current Rt of 1.5): our winter visitors arrived in the autumn and are still with us; and, schools have been open, more or less.

    But, the winter visitors tend to be elderly, and likely to take more precautions than younger people, due to their higher risks. And, the schools have been closing back down.

    Our hospitals are stressed, and if that 1.5 persists, will be grossly overloaded within two weeks.

    How do you explain the shape of this?

    http://91-divoc.com/pages/covid-visualization/?chart=states-normalized&highlight=Arizona&show=highlight-only&y=both&scale=linear&data=cases-daily-7&data-source=jhu&xaxis=right&extra=&extraData=deaths-daily-7&extraDataScale=separately#states-normalized

    • Meso,
      Arizona has seasonal influenza outbreaks just like other states have them. Whatever factors apply to seasonal outbreaks apply to Arizona also.

      • I’m trying to explain the unusual seasonal influences here. Why should we have two large peaks at opposite peaks of our seasons. In other words, one when the temperatures are too hot to do much outdoors, and the humidities are very low, and the other when the temperature are very nice outdoors, and the humidities are low.

        I don’t see how the weather/climate is the big factor.

      • Joe - the non epidemiologist

        Meso – the two peaks in AZ (not three) are following the Hopes Simpson curve with the exception that the divide appears to be the 33rd/34th parallel instead of the 30th parallel. The same is true for rest of the southern states. California being an exception. Granted it is not a complete explanation, but it does provide some insight. The exceptions to the hope simpson curve are those regions/countries that had an extremely hard lockdowns.

      • “Meso – the two peaks in AZ (not three) are following the Hopes Simpson curve with the exception that the divide appears to be the 33rd/34th parallel instead of the 30th parallel. The same is true for rest of the southern states. California being an exception. Granted it is not a complete explanation, but it does provide some insight. The exceptions to the hope simpson curve are those regions/countries that had an extremely hard lockdowns.”

        I apologize, but I have no idea what explanation you are offering. Could you explicate it a bit?

      • “Meso, Arizona has seasonal influenza outbreaks just like other states have them. Whatever factors apply to seasonal outbreaks apply to Arizona also.”

        Yes, it does. But we don’t get influenza in the summer, just the winter. But COVID19’s main spikes here were in early July and now (early January). It is possible that our winter influenza peak is imported from other states, especially with our snowbird visitors (part year residents who come from cold climates).

        So, something is affecting COVID19 differently, which is one reason I am puzzled.

      • Joe - the non epidemiologist

        dougbadgero – figure 2 on page 39 of your link

        Figure 2 matches very closely with the with what is happening in the US and Europe. The top graph in Fig 2 is matches the northern states, while the second graph matches the southern states. The only large difference is the dividing line is 30 parallel in Hopes simpson while covid dividing line seems to be approximately the 33rd-34 parallel. ( I cant explain the difference in the 3degrees of latitude)

        I cant explain the reason, nor will I attempt any explanation other than nature having a bigger hand in the curve than man’s ability to control it.

        Numerous individuals have been critical of Nic for being “wrong” about herd immunity. Possibly, Sweden appeared to have reached herd immunity based on the reduction in the IFR in the late spring and during the summer. Sweden’s curve likewise matches very closely with the Hope Simpson curve for the northern latitudes along with the rest of europe,with the exception of norway and finland. Note that I am neither praising or condemning Nic. (he is vastly more knowledgable than I am with both stats and epidemiology, so it is certainly not my place to condemn his work)

      • Joe - the non epidemiologist

        Meso – The hope-simpson curve shows for regions south of the 30th parallel a summer peak approximately 1/2 of the winter peak, while regions north of the 30th parallel to have a single peak in the winter months with no summer surge.
        All the southern states such as AZ, TX, GA, FL, AL, MS, CA. The difference in the southern states surge vs the historical trends detailed by Hope-simpson is the dividing line for the summer surge is the 30th parallel vs the dividing line to be approx the 32/33/34th parallel with covid. I cant explain the reason, only pointing out the similarities.

  14. I’m thinking… it’s the result of the onset of winter and a mobile population…

    • What people are still flying here, although flying is down quite a quite a bit, are from all over the world…

      • For example, I talked to a couple who called off a family gathering prior to Thanksgiving because a couple of members had come down with covid-19 and they blamed that on a local restaurant in their area but as it turned out the wife of the couple also worked for a company that had been visited by persons from Turkey with whom they spent the day and night with and that country was on a government warning list and they had probably flown to the US through Paris or Frankfurt and no telling how many days layover they might have taken to enjoy the trip so… If you’re older and no longer working you probably are safer as far as being insulated from potential carriers unless you’re in a rest home in which case you probably have no control over people coming in and bending over you and coughing in your face…

  15. In 2020, we had 1001 cases on Vancouver Island (Population 870,000) and 12 deaths. We actually beat back introduced viruse here 3 or 4 times during the summer, and we had one streak of 100 days with no infections. That was pretty good! Vancouver (population 3 million’ish) didn’t do so good even though we are under a little less restrictions than they are. Lots of people retire here so we have been masking up a lot, so the old people don’t bite the dust. Also, its a bit harder to travel here (ferries or planes) and people here tend to key (or generally vandalize) the cars of people who pop over for a visit. BUT now the UK variant is already here so our winning streak is over. The R value will obviously increase now the more transmissible version is here. So it spreads quicker AND the herd immunity value goes up from maybe 70% to say 90%. BUT when the South African, Nigerian, and Brazilian variants mix, then “survival of the fittest” means an even more transmissible, or longer lasting or more gradual (or more rapid) Super infection will develop from the combination. Maybe a variant will develop that leaves minimal immune response. Viruses don’t have sex per say, but they do the recombination thing if someone gets infected by 2 strains at once, so a new strain or combination of strains may win the race. This is inevitable. I don’t get why we western humans make it so easy for the different strains to mix. We couldn’t have done it better if we tried. Planes come in to Victoria (where I live) every week with Covid. Container Trucks come up from the usa every day too. I have no idea why they don’t change “tractors” at the border. That would really reduce mixing. Its Crazy. Anyway, the “math” Nick is doing is a curiosity. It means nothing, mainly because western countries don’t have common sense leadership who think to reduce “mixing” of the viral races by banning international travel, sensible truck rules, etc etc. It really isn’t that hard. A lockdown needs to be real, not half hearted. We never had a real one. It may be too late now. .

  16. I cant help but think, maybe erroneously, that 3000IU of vitamin D daily, whilst not stopping Covid-19 would serious reduce its adverse effect

  17. China deletes Wuhan lab research data in desperate cover up of source of covid-19:

  18. Pingback: COVID-19: why did a second wave occur even in regions hit hard by the first wave? – Climate- Science.press

  19. Many people, myself included, thought that in the many regions where COVID-19 infections were consistently reducing during the summer, indicating that the applicable herd immunity threshold had apparently been crossed, it was unlikely that a major second wave would occur.

    Many? Almost nobody thought this in the scientific community.

    Many people in the right wing libertarian political community and their well financed media friends *wanted* to believe it, sure.

    • Untrue. If you bothered to look at the references that I cited, you would see that they are both by mainstream academic authors. One of them is in Nature Communications, which to my knowledge is not somewhere the ‘right wing libertarian political community’ (whoever that may be) are normally able to publish.

      • Untrue.

        You don’t specify which references you rely on, but the nature Comms article absolutely does not support your “unlikely that a major second wave would occur” assertion in any way whatsoever.

      • Essentially what you’re claiming here is that many in the scientific community believed that herd immunity was what drove the reduction in infection seen in many regions.

        That’s just not true at all.

      • According to the govt dashboard and research by a third party, the levels of infection are thought to be far higher than originally thought in many parts of the country

        https://www.dailymail.co.uk/news/article-9127059/One-FIVE-people-coronavirus-modelling-suggests.html

        I don’t know either way, so just putting it out there as maybe relevant

        tonyb

      • Tony,

        No idea what relevance that has to previous views on herd immunity and 2nd waves. Those figures aren’t far off what was previously thought btw, I haven’t looked into what’s behind them, and I doubt the Mail will help that…

      • @verytallguy
        Re: “Essentially what you’re claiming here is that many in the scientific community believed that herd immunity was what drove the reduction in infection seen in many regions.
        That’s just not true at all.”

        Exactly. It was mostly an idea peddled by right-wing politically-motivated non-experts, and people who’s ideas were so obviously wrong that they couldn’t get through peer review or any review by informed experts. A second wave was shocking to those ideologues + non-experts, but not to those of us who knew better than to think Sweden, Stockholm, New York City, Geneva, etc., reached the herd immunity threshold.

        Instead, informed people were pointing out that the herd immunity threshold was not very low, one should not allow rely on natural infection (as opposed to vaccination) as means of achieving some fictionally low herd immunity threshold, and that one should pursue the sort of mitigation policies Lewis opposed (instead of allowing people to get infected to achieve herd immunity).

        It is simply absurd for Lewis to act as if much of the scientific community was behind him on his obvious errors:

        “The arrival of a second wave and the realisation of the challenges ahead has led to renewed interest in a so-called herd immunity approach, which suggests allowing a large uncontrolled outbreak in the low-risk population while protecting the vulnerable. Proponents suggest this would lead to the development of infection-acquired population immunity in the low-risk population, which will eventually protect the vulnerable.
        This is a dangerous fallacy unsupported by scientific evidence.”

        https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32153-X/fulltext

        “In summary, there are large differences in patterns of per-capita deaths in different countries that are difficult to reconcile with herd immunity arguments but are easily explained by the timing and stringency of interventions. Seroprevalence studies also provide an independent source of information that is highly consistent with mortality data. The herd immunity argument is therefore at odds with both mortality and seroprevalence data, whereas the intervention argument provides a parsimonious explanation for both.”
        https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)31357-X/fulltext

        “Countries should not rely on herd immunity by natural infection to suppress the epidemic. The disease and death that would accompany natural infection rates to reach herd immunity, typically estimated as 40–60% of the population infected, would be unacceptably high. Uncertainty also remains about the duration of acquired immunity from past infections.”
        https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)31927-9/fulltext

        “Initially, some local authorities and journalists described this as the herd immunity strategy: Sweden would do its best to protect the most vulnerable, but otherwise aim to see sufficient numbers of citizens become infected with the goal of achieving true infection-based herd immunity. By late March 2020, Sweden abandoned this strategy in favor of active interventions; most universities and high schools were closed to students, travel restrictions were put in place, work from home was encouraged, and bans on groups of more than 50 individuals were enacted. Far from achieving herd immunity, the seroprevalence in Stockholm, Sweden, was reported to be less than 8% in April 2020,7 which is comparable to several other cities (ie, Geneva, Switzerland,8 and Barcelona, Spain9).”
        https://jamanetwork.com/journals/jama/fullarticle/2772167

        “In light of these findings, any proposed approach to achieve herd immunity through natural infection is not only highly unethical, but also unachievable.”
        https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)31482-3/fulltext

        “At present, herd immunity is difficult to achieve without accepting the collateral damage of many deaths in the susceptible population and overburdening of health systems.”
        https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)31483-5/fulltext

        “This quasi-equilibrium is maintained not because of herd immunity but because of changes in behavior. […]
        The peaks occur at levels of infection far from that associated with herd immunity. Post-peak, shoulders and plateaus emerge because of the balance between relaxation of awareness-based distancing (which leads to increases in cases and deaths) and an increase in awareness in response to increases in cases and deaths.”

        https://www.pnas.org/content/117/51/32764

        “This allows us to “bend the curve” and predict temporary equilibrium states, far away from the equilibrium state of herd immunity, but stable under current conditions […]. Yet, these states can quickly become unstable again once the current regulations change.”
        https://link.springer.com/article/10.1007/s00466-020-01880-8

        “Transmission dynamics reveal the impracticality of COVID-19 herd immunity strategies”
        https://www.pnas.org/content/117/41/25897

        “Four months into the COVID-19 pandemic, Sweden’s prized herd immunity is nowhere in sight”
        https://journals.sagepub.com/doi/10.1177/0141076820945282

        “There are two possible approaches to build widespread SARS-CoV-2 immunity: (1) a mass vaccination campaign, which requires the development of an effective and safe vaccine, or (2) natural immunization of global populations with the virus over time. However, the consequences of the latter are serious and far-reaching—a large fraction of the human population would need to become infected with the virus, and millions would succumb to it.”
        https://www.sciencedirect.com/science/article/pii/S1074761320301709

        “Vaccination is the only acceptable path to herd immunity”
        https://www.cell.com/med/fulltext/S2666-6340(20)30032-5

        “Consequently, herd immunity may be the most important “short term” strategy to protect this portion of the population [97]. Until a safe vaccine is developed, research on specific new treatments (or an effective combination of existing treatments), together with action plans to contain the spread of the virus, seem to be the only alternatives for protecting at-risk populations [98].”
        https://www.mdpi.com/2076-393X/8/2/236/htm

        “Public health organizations condemn herd immunity scheme for controlling spread of SARS-CoV-2”
        https://apha.org/news-and-media/news-releases/apha-news-releases/2020/public-health-orgs-condemn-sars-covid2-plan

        “Attempts to reach ‘herd immunity’ through exposing people to a virus are scientifically problematic and unethical. Letting COVID-19 spread through populations, of any age or health status will lead to unnecessary infections, suffering and death.”
        https://www.who.int/news-room/q-a-detail/herd-immunity-lockdowns-and-covid-19

        “Early herd immunity against COVID-19: A dangerous misconception”
        https://coronavirus.jhu.edu/from-our-experts/early-herd-immunity-against-covid-19-a-dangerous-misconception

        “We do not endorse the idea of letting the epidemic a free hand in order to create sufficient herd immunity to end the epidemic; as it would entail an enormous burden on the healthcare system – United Kingdom, at first, considered a different approach – of unrestricted spread of disease without any brakes applied, but public health experts were able to convince the government to accept the more reasonable mitigation approach. We should clarify at the outset that an approach of uncontrolled spread, one that no country is following at this time, would be a terrible mistake.”

        Click to access 505.pdf

      • Ugh, the politicized attacks on Lewis are getting more odd just as they seem to be getting angrier.

        Let’s take one piece of “evidence” from one of the attackers:

        “There are two possible approaches to build widespread SARS-CoV-2 immunity: (1) a mass vaccination campaign, which requires the development of an effective and safe vaccine, or (2) natural immunization of global populations with the virus over time. However, the consequences of the latter are serious and far-reaching—a large fraction of the human population would need to become infected with the virus, and millions would succumb to it.”
        https://www.sciencedirect.com/science/article/pii/S1074761320301709

        This is supposed to be devastating to the claim that herd immunity can be reached and someone ought to attempt to figure out when. Guess what happens absent a vaccine? The only option is natural immunization. Guess how many times Nic Lewis argues against a vaccine? None.

        Oh, but this is supposed to back up the claim that there is a third way! Our enlightened political leaders can force the virus away by strictly controlling the population. You’ll note they don’t actually say that anymore because it’s obviously not true. The places with the most strict lockdowns and most complete mask adoption are the places with the highest spread. This is true in and out of countries with someone named Trump.
        These clear facts somehow have no impact on the willingness of the “pro-science” sect to bow to it daily.

        There are a few things we know about Covid and immunity- we cannot stop the spread of a highly infectious virus with mandates (but those can destroy lives, economies and schools), we didn’t reach natural herd immunity, the American president’s work to rush a vaccine was critical and will save hundreds of thousands of lives thereby giving the American media a reason to praise Andrew Cuomo for no reason even more vigorously.
        Ironically the one political leader most prescient about Covid spread was Angela Merkel, who told the press at the start of all this that 80% of Germans would get Covid.
        Nic quibbled with the number 80 for science reasons and tried to come up with the right one.
        VTG and his sidekick, meanwhile, still seem to be insisting that Los Angeles must be covid-free because the government ordered people not to spread it.
        Why they think that makes them more accurate than Nic Lewis is beyond me.

      • Matthew R Marler

        Atomsk’s Sanakan: It is simply absurd for Lewis to act as if much of the scientific community was behind him on his obvious errors:

        Good posts. However, Nic Lewis only claimed that “many” people disputed the possibility of a second wave, and were wrong; and he was wrong. What think you of the reasons he gave for explaining how he and they came to be so wrong?

        Did anybody make accurate quantitative projections?

  20. UK-Weather Lass

    I have always found Mr Lewis’s papers interesting and that feeling is enhanced when he works his statistical magic through a real time event in which we are all participants. This paper is another thought provoking look at how we have continued to struggle to find a way out of the cul-de-sac of our own making.

    My own experience of this novel virus’s ability to confound its chosen host in ways I have never before seen in the UK (or elsewhere) is unsettling. At one extreme my experience has been watching the fear of this virus from people who have little chance of contracting a serious episode of illness but could ostensibly asymptomatically transmit the disease to a more vulnerable person if such an event is possible. At another extreme has been the inadequacy of our public health response especially test, trace and track, where, at every stage, poor quality is and has been demonstrably present throughout while there has been little evidence of strong professional and experienced leadership at any level. Surely that has damaged much of the evidence that has been offered to us via these outsourced services both in blighted test results and inadequately finding and tracking those who have received a blighted test result be it false positive or false negative. Who is in charge of quality control and do they have the necessary experience, expertise and leadership ability?

    Even the stance on variants of the virus, which virologists say can be frequent and numerous in all cases of a coronavirus, has been turned into an additional terrifying event which must, according to politicians, be stopped, not because the variant is known to be more or less pernicious but because our hospitals could be overwhelmed (almost exactly a year after they were previously about to be seriously overwhelmed so no change there then).

    My take on this whole experience is that we have been badly let down by ruling classes that spend much of their time looking down and exercising thumbs and not enough of their time looking up and actually observing what is going on with people around them. They need to observe the fine detail of how we once accepted that the people who get sick and recover become a part of the crowd who have always in many senses eventually protected the more vulnerable. We can act to save lives and unnecessary deaths but the measures we have taken have simply failed to do that and the reasons are to be found in how we have attempted to manage this epidemic – forever murmuring OMG – instead of getting on with life as we know and accept it and riding the attack out.

  21. Pingback: COVID-19: why did a second wave occur even in regions hit hard by the first wave? – Watts Up With That?

  22. Seriously. Does anybody even have to ask this question?

    I’ve been saying there would be second waves and probably even third waves and possibly even fourth waves.

    As cases ebb from one wave, constraints on the spread start to loosen sometimes by deliberate government action but, if not that, then by the human tendency to think the disease has passed. As constraints loosen, new previously unexposed groups of people get ill and the next wave begins. Constraints tighten again through government action or fear so the cases begin to ebb again. The cycle repeats until there is herd immunity or enough unexposed people impose sufficient restraints on interaction that the virus no longer exists in the population. Wiping the virus out of the population is extremely difficult in a mobile free society.

    Restrictions until enough people are vaccinated to reach herd immunity is the only option that will not result in significant numbers of deaths.

  23. Pingback: COVID-19: why did a second wave occur even in regions hit hard by the first wave? |

  24. > Since neither of the factors on which the HIT depends are fixed, the HIT is not fixed either.

    Without any attempt at explication of reasonable parameters, that statement could mean that “HIT” could be almost anything and thus “HIT” seems pretty much meaningless.

    I’m wondering if anyone can provide an example in history where “natural herd immunity” has been considered established only later to see the kind of surge in infectiousness on the order of what we’ve seen with COVID after there were claims that “herd immunity” had been established.

    Keeping in mind, of course, that “herd immunity” is only really a meaningful term if it is being applied to a condition where a real society is engaging in at least close to normal behaviors.

    I’m going to wager that no one can come up with one (absent something like a major new mutation coming into play).

    So then the question would be why are people trying to say that the massive growth in infections we’ve seen is some how an outgrowth of “seasonality.” Is COVID considered somehow uniquely subject to seasonal variation? I don’t recall anyone making a scientific argument to support that view.

    Or maybe having seasons is some kind of a new thing? I mean I know that 2020 was someone of a unique year in many respects and 2021 is off to an unusual start….

    • Without any attempt at explication of reasonable parameters, that statement could mean that “HIT” could be almost anything and thus “HIT” seems pretty much meaningless.
      However, I have made such an attempt in my article. Why don’t you propose what you think are reasonable parameters if you disagree with my suggestions, rather than always just sniping unpleasantly?

    • “Keeping in mind, of course, that “herd immunity” is only really a meaningful term if it is being applied to a condition where a real society is engaging in at least close to normal behaviors.”

      Which is exactly why I focussed on data from Sweden in previous articles.

      • Which is exactly why I focussed on data from Sweden in previous articles.

        The view of Sweden as a libertarian fantasy of freedom is not, and never has been true.

        https://ourworldindata.org/grapher/covid-stringency-index?tab=chart&stackMode=absolute&time=2020-01-22..latest&country=SWE~GBR~NOR&region=World

      • I am not sure about them being a Libertarian fantasy, but they are continued evidence that lock downs don’t seem to make much difference.

      • Posted that way upstairs. Along with some interesting Tweets from Tom Frieden…

      • > Which is exactly why I focussed on data from Sweden in previous articles.

        Except Sweden wasn’t really that close to “normal” behavior even by Swedish standards – high schools and colleges closed, large groups not allowed, massive numbers of people working at home, people traveling less in ways that paralleled other similar countries, people not allowed to visit seniors in congregate housing, ill seniors basically just allowed to die rather than transported to hospitals, etc.

        And even “normal” in Sweden is far from “normal” in countries which you prescribed should follow Sweden’s lead, based on your mistaken conclusion that they’d reached herd immunity. Probably age stratification is the most important predictor and many other countries are sort of “normal” when compared to Sweden in that aspect, but there are myriad other important predictors which, IIRC, in all you articles on this issue have NEVER even come remotely close to discussing. Even the most obvious ones such as household size or # of multi-generational households.

        So it’s a bit hard to see how you took this issue of the generalizability of Sweden’s “normality” very seriously. Looks more likely to me that you simply made some convenient assumptions.

      • I do admire Nic for responding to the vague, pseudoscientific and largely rhetorical criticism here.

        Kwok in the Nature piece I quoted above says that the HIT was highly variable on a country by country basis, ranging from 5% to 78%.

        The problem with using R0 is that it is ill defined and not useful. It will depend strongly on the month in which the epidemic started to give just 1 example for any seasonal virus.

        ‘Normal conditions’ likewise is impossible to define rigorously. Just as trying to define a single weather ‘normal condition’ is impossible or meaningless for telling you what the weather will be tomorrow.

    • Re: “I’m wondering if anyone can provide an example in history where “natural herd immunity” has been considered established only later to see the kind of surge in infectiousness on the order of what we’ve seen with COVID after there were claims that “herd immunity” had been established.”

      It doesn’t happen. As I pointed out months ago in July ( https://medium.com/@silentn2040/the-dangerous-myth-that-sweden-achieved-herd-immunity-fd2579526b8b ), multiple peaks during public health interventions and behavior changes that push one away from the baseline conditions of R0 that define herd immunity, are a clear sign herd immunity wasn’t achieved. It’s telling Lewis just got around to addressing this, yet still did so badly. Almost as if he didn’t understand the basics of the epidemiology, immunology, infectious disease, etc. he was distorting…

      “If significant herd immunity developed following initial major water supply contamination, a multipeaked and/or prolonged epidemic would not be expected to occur [page 722].”
      https://www.sciencedirect.com/science/article/abs/pii/S0168827894802297

      https://mobile.twitter.com/AtomsksSanakan/status/1332738821998456841

    • A lot of vague non science in this thread. I still think the weight of evidence is pretty clear. There has been nothing substantive here from the usual nonscientists to even attempt to explain the course of the epidemic over the summer in the US. Especially in New York and California, where restrictions if anything have gotten more severe over time, there has been a strong surge this winter and a relative lull over the summer. The story is much the same in virtually every European country. The quite marked summer lull in the epidemic is a rather glaring feature which requires an explanation.

      Nic has at least tried to outline the science and the math. And the response is a bunch of ankle biters raising baseless objections.

  25. It is remarkable how challenging the coronavirus is proving to gain understanding of its epidemiology. What’s clear is that we’re not out of this, not by a long shot.

    λ will always exceed 1 (its level in a homogeneous population); pre-epidemic λ may be ~4.

    Small mathematical point: a parameter with range from 1 to infinity is poorly framed. Like degree of anisotropy for example (maximum over minimum eigenvalue). It would be better for it to range from 0 to 1 so it is well bounded and can be converted into a percentage. This is easy to do – just invert the ratio and precede it by “1 minus”.

  26. Pingback: COVID-19: why did a second wave occur even in regions hit hard by the first wave? – Climate- Science.press

  27. The vaccine will give us some hard data on HIT. In my state the count of those vaccinated plus those who caught the virus is ~7% and new case counts are still high.

    It’s interesting watching all the commenters on this site who told us lockdowns and masks would stop any new wave of the virus attempt to pick on Nick for trying to estimate herd immunity.
    I know a bunch of people in California. They are all locked down tighter than we ever were in my city and I think they all wear masks even when alone. Their hospitals are discussing how to ration ventilators right now. While CNN attacks the governor of Florida for no reason. Again.

    A whole lot of people were wrong about this virus

    • “ I know a bunch of people in California. They are all locked down tighter than we ever were in my city and I think they all wear masks even when alone. Their hospitals are discussing how to ration ventilators right now. While CNN attacks the governor of Florida for no reason. Again.”

      Something we have all gotten use to. There are no facts anymore. No truths. Just narrative and propaganda. The MSM have no idea how much they are despised.

      • And people are getting really angry and real damage is being done.

        Our blue governor flip-flopped on school reopenings (and looks to be flipping back again). Before the election, anyone who wanted the schools open was a raving Trumpist. After the election, they tell us the “science” says reopening schools is safe and necessary. Which is true of course, in fact it always was, but because they denied it so vocally for so long we literally have petitions flying around my city with left, right and center angrily waving pre or post November “the science” at each other and calling each other evil liars. The only bi-partisan agreement is that “the science” is wrong. Great job everyone!

        They better get the vaccine out faster. After the end of January they won’t have anyone to hate as a distraction from all the damage they’re doing.

      • Can you imagine if CNN had their brainwashing operations going in 1941?

        https://pbs.twimg.com/media/EgjbdziXYAYlMUH?format=jpg&name=900×900

    • HIT from a vaccine will be different than HIT from those who get the infection. Especially, when we are vaccinating the least socially connected people. This makes some sense of course since they are also the most vulnerable.

    • jeffnsails850,

      I live in LA County, which right now seems to be the COVID capitol of the USA. I do not feel “locked down” (shades of Brian White’s comment https://judithcurry.com/2021/01/10/covid-19-why-did-a-second-wave-occur-even-in-regions-hit-hard-by-the-first-wave/#comment-939548 below).

      Yes, we have a lot of businesses shut down, but I still go to the supermarket, Walmart, Target, Costco, and so on (the bigger stores). Malls are open, and I can get take-out food from the Food Court (I miss Sbarro stuffed pizza). I even went to a Farmers’ Market this morning in La Canada. Yes, masks are required in stores, but the “quality” of the mask is unimportant. Just as long as you have your nose and mouth covered, and the nose part seems often optional. Checkers at Trader Joes can bag your groceries now as “fomites” have apparently been deprecated as a mode of transmission. More people seem to be allowed in stores as well. Rush hour traffic has been almost normal for awhile as cars are no longer social-distancing.

      If I go hiking at the regional park in Montecito Heights or even walking down the street, my mask is under my chin. Some stay at home order! I’m not aware of any police enforcement. Maybe “large gatherings”, but I don’t go to them anyway.

      Weather doesn’t seem to matter, even if Joe – the non-epidemiologist’s comment about latitude and seasonality is right. We have had summer-like weather here for awhile and very little cold weather. But cases are “surging” if the PCR test is to be believed. And all those scientific expectations of an autumn / winter surge don’t seem to have influenced hospital planning much.

      Very crazy times.

      • Joe - the non epidemiologist

        LA Bob – “Weather doesn’t seem to matter, even if Joe – the non-epidemiologist’s comment about latitude and seasonality is right.”

        Bob – I am not claiming to be right as much as pointing out the observation that the covid surges/trends seem to be following century old historical trends with prior influenza viruses.

        There is an old saying that you cant know where you are going if you dont know where you have been. In the case of Covid, it seems the experts have thrown out all the science about prior influenza pandemics and substituted “the new Covid science” .

        The other observation is that the Covid surges seem to be following historical trends (albeit on a larger scale due to covid novelty) irrespective of the level of mitigation instituted by the states governors and the level of compliance with those mitigation steps. For example colorado and Minnesota had very similar covid surge trends in the fall with most of the other northern states with vastly different levels of mitigation compliance.

      • Thanks for your first hand report from LA. I lived in LA for a few years, so I’m familiar with the excellent climate.

        “Weather doesn’t seem to matter, even if Joe – the non-epidemiologist’s comment about latitude and seasonality is right. We have had summer-like weather here for awhile and very little cold weather.”

        Yeah, I have a problem too with the seasonal hypothesis. LA barely even has seasons until you get inland some distance from the ocean. Even then, the temperature variation is pretty mild.

        I’m in Phoenix. We are at the same latitude as LA, and have the same annual rainfall (yes, technically, LA is in a desert).

        And we are also having a surge – almost as bad as that in LA county. You have about 140 new cases per day, and we have about 120.

        Interestingly, both of our states had both a summer and a winter surge, but our summer surge was about double LA county’s. (graphs on us-covidtracker dot com).

        So, I find that a hypothesis tying the seasons to our epidemic to be weak – at least if we define seasons in terms of weather. As I’ve mentioned here before, we have had a peak at our hottest time, and now we have one at our coldest time. We’ve had no precipitation at all during those peaks.

      • Manaus has had huge surges in opposite seasons.

      • Joe - the non epidemiologist

        Manaus Brazil which is near the equator , the trends for covid are following The hope Simpson surge trends for the regions 0 to 30s latitude – albeit on a larger scale. ( reasonably close with some variation , ie similar trend)

      • Hi, Joe,

        I poked around a bit in order to understand Hope-Simpson, who at least had the temerity to question common views on influenza transmission. In light of the 30th parallel thing, I’m trying to understand Brazil’s Worldometer numbers. Brazil fits almost entirely above the 30th parallel in Southern Hemisphere terms, and they should be in their summer season now, correct? But their daily new cases are trending higher than last winter, and daily deaths are trending about the same.

        I suppose one might suggest that Brazil got a later start on COVID-19 than Northern Hemisphere countries, so there’s not been enough time to establish a pattern.

        I also found this 2008 review, which seeks to explain Hope-Simpson flu seasonality by vitamin D, which seems to be a hot topic these days.

        https://virologyj.biomedcentral.com/articles/10.1186/1743-422X-5-29

      • Joe - the non epidemiologist

        LA Bob – my purpose in mentioning hopes simpson is to bring some historical insight into influenza.

        Brazil is mostly north of 30S. I have attached a link to his study (page 39, figure 2) which shows 30s -0 to have the its surge in June/July, with a summer surge of Nov/Dec/Jan. with both surges to be rather flat vs the strong surges north of 30N and south of 30s.

        The charts for brazil at 91-divoc . com shows a somehat similar trend for brazil . Unfortunately, I couldnt pull a chart for just manasus.

        I also agree with your comment regarding Vitamin D,

      • I ask again: how does Hope Simpson apply to Arizona. Specifically, since the population is in a narrow latitude band (33N), Maricopa County, AZ.

        Again, we had a large peak in the summer and are experiencing an even larger peak now in the winter.

        Also, do you have a reference to Hope Simpson (I assume you mean Hope-Simpson)? I find a Wikipedia article that doesn’t address the issue:

        “His career-long interest in the manner of transmission of the influenza virus was first stirred by the great epidemic of 1932–33, the year in which he entered general practice. It culminated in a book, published in 1992, which questioned the theory of person-to-person transmission being enough to explain the simultaneous appearance of influenza in places far apart. His initial hypothesis proposed that the cause of influenza epidemics during winter may be connected to a seasonal influence.[2] His later research suggested that the correlation may be due in part to a lack of vitamin D during the wintertime, after documenting that influenza A epidemics in temperate latitudes peak in the month following the winter solstice.[3][4] His findings were based not only on observation in his practice, but also on extensive historical research into past epidemics”

        I note: “questioned the theory of person-to-person transmission being enough to explain the simultaneous appearance of influenza in places far apart.” sounds like quackery, given that it was published in 1992.

  28. The Covid Tracking Project presents and interesting graphic (https://covidtracking.com/data/charts/deaths-per-million-by-state) which shows that the states hit hardest by Covid-19 early (New York, New Jersey and Connecticut) not only had dramatic fall-off in death rates through the summer, but have not seen an anywhere near comparable rise in death rates associated with the apparent seasonality of the virus. States that experienced dramatic rises in death rates in November and early December (North and South Dakotas and Iowa) also saw death rates fall rapidly. Suggests perhaps a degree of herd immunity does come into play. The latter three states have tended to be much less restrictive regarding Covid-19.

  29. Useful twitter thread showing (lack of) correlation of first wave infections/deaths to second wave, using US data

  30. The UK is seeing higher numbers of deaths with a positive covid test than in April, but overall mortality is now back to normal for the time of year in England, below normal in Wales and N Ire, and way below normal in Scotland. I guess they must have Dominion machines counting all these new cases and covid deaths.

    At the foot of the page:
    https://www.euromomo.eu/graphs-and-maps

  31. Where is the mention of the mandated PCR test cycle count (amplification rate), and it’s impact on positive testing results and influence on the 2nd wave data? IIRC, the CDC/FDA held the recommended level to a 40 Ct rate until recently.

  32. Everett F Sargent

    “However, increased population immunity resulting from some combination of further spread of infections and vaccination programmes, the combination varying from one country and region to another, should bring COVID-19 epidemics under control within the next few months.”

    You get a choice (1) Forgo the vaccine entirely and reach HIT naturally or (2) take the vaccine and reach HIT artificially. Standard practice has been to go with (2) as that is what the vast majority of MD’s imply when speaking of herd immunity. /: Just like every other infectious disease has been treated in the past since at least the polio vaccine. This is a prime example of the fallacy of moving the goal posts. As perhaps tens of millions would have died if we even tried to reach HIT via letting nature take its course and living your life as if there was nothing to see here please move along to your own more probable death.

  33. Geoff Sherrington

    Those who claim that lockdowns have no effect on the progress of the virus should study Australia’s management. One has to be careful to define what can be seen as a “gain” or “success”. Australia currently has varieties of lockdown in all of its 6 States, who control management of the virus. There are currently under 10 new cases a day reported nationwide. But is this success? It might be no more than an artificial adjustment of the time base, meaning that we are simply postponing an inevitable worsening, at the economic cost associated with lockdowns. The hope is that future vaccination, due to start in a month or so and end 6 months later, will make theories of acquired herd immunity somewhat irrelevant. That is a hope yet to be tested.
    However, Australia and New Zealand are strong examples that lockdown can be used to recover from a growing infection rate and to hold that rate close to zero, for whatever the purpose might be.
    BTW, I deplore the actions of those here who are critical of Nic Lewis in a nasty, personal way. Nic is attempting to clarify a complicated piece of medical science that has some math features best handled by a mathematician. The eventual understanding of the problem is not helped by criticism of those trying to help understanding. Modern science has attracted many commentators of unproven scientific ability who seem to enthuse about cancel culture processes developed, inter alia, in or around the climate science community, which will not end up well regarded in the fullness of time. Geoff S

  34. I agree with Geoff…Joshua you are way off base with your attitude…It’s simple without getting your undies all bunched up… nick had a thought about transmission of the virus relating to heard immunity…. turns out that idea was probably not so good in hind sight….so what….I can’t find anyone, anywhere who has accurately predicted the path of the Pandemic. Would be nice if you took that little bit of “vengence” that pervades your on-line persona somewhere else. It’s a problem you have to work out on your own.

    • Josh is often illogical, very biased, often wrong, and talks way too much. Word count to substance ratio is very high even for anonymous nonscientist ankle biters. That he thinks he can show such disrespect to serious scientists is arrogant.

    • SteveS –

      > turns out that idea was probably not so good in hind sight…

      Except it’s not only in hindsight. I agree that there’s certainly nothing wrong with Nic using his talents to build toy models of COVID spread. But he made obvious errors that were pointed out quite a while ago and for.mknths he resisted accepting evidence that obviously ran in contrast to his theories.

      Further, he used his science in support of policy advocacy, and again in his policy advocacy he selectively ignored uncertainties that ran counter to his preferred policies.

      So that’s life. And and just as. I one present perfect analysis, there’s also nothing wrong with criticizing his analytical output nor with questioning whether his work reflects ideological biases.

      Hopefully criticism can be offered without turning personal but that gets tricky when someone offers analysis that is presented as bias free yet reflects bias. I’m certainly not going to cry about it if someone feels I’ve done do unfairly and calls me on it and I’m more than happy to examine whether I’ve stepped over the line.

      On the other hand. Nic is a big boy and I’m sure the he can easily get past it if I’ve said something that’s unfair.

    • Josh, You pointed to no specific scientific or mathematical error. You spent many hours of your precious life on sarcastic and very vague accusations with no specific point.

      • >You pointed to no specific scientific or mathematical error.

        I certainly pointed to no mathematical error. I don’t question the validity of Nic’s math, as I wouldn’t be able to do so. Nor do I question the value of him doing mathematical explorations of the pandemic. He has the potential to either make an extremely valuable contribution through providing new insights into how the pandemic might play out, or to make less important but useful contributions by showing the ways that people can erroneously model the pandemic.

        As for “specific scientific errors,” I think I have pointed to specific mistakes he made in his scientific processes – in his treatment of uncertainties and in his weaknesses in controlling for potential biases. Those are fundamental scientific errors. For example, it is a specific scientific error to extrapolate from unrepresentative samples, as I think he has done numerous times in his posts on COVID.

        At any rate, it’s obvious that Nic made specific scientific or mathematical errors in his analyses even if I haven’t outlined exactly what they are. That’s obvious because the conclusions that he drew were falsified by reality, subsequently. I’d guess that for the most part, his errors where in misguided assumptions about human behavior or a lack of respect for the uncertainties involved in projecting mathematical calculations into complex social contexts – but that’s to be expected when someone attempts an endeavor such as Nic attempted. That doesn’t mean I’m criticizing him for making an attempt.

        My point is that he should have been (1) more circumspect about his ability to model complex behaviors amid an incredibly complex social context (2), been more open to looking at how unfolding events conflicted with his theoretical analysis and most importantly, (3) been more careful to at least attempt to control for obvious confounding variables before attempting to project the impact of his modeling mixed with his personal feelings about optimal social policy, so as to prescribe that his preferred policies be followed. It’s certainly his right to do all of that. Just as it is certainly my right to criticize him for doing so. I don’t particularly care whether you like it or not if I do that.

        Again, my primary criticism is that Nic looked at Sweden, mistakenly simplified how the behavioral context influenced the presumptions underlying his modeling, and then prescribed policies based on having done so. And also I think he should have been less resistant to integrating how events unfolded into his analytical processes.

        But worse, in doing so he ignored the obviously confounding factors that would enter into play as he prescribed extrapolating from Sweden to advocate the for implementation of similar policies in other, vastly different contexts. Of course, he has the right to do all of that. But given that a US Senator pointed to his mistaken modeling as a partial basis for developing US policies, the implications became real in terms of how lives are affected.

      • Epidemiology is a very inexact science with huge uncertainties. Given the ill-posed nature of the problem and the models, Nic has done a reasonable job here of showing how uncertain the math shows things to be. He did that too with regard to the earlier posts on heterogeneous susceptibility and Sweden. Perhaps you are just too math illiterate to understand the implications of that math. This is why the scientific community as a whole has a bad track record with regard to predicting the course of this epidemic.

        Do you have no idea what an uncertainty analysis is? A simple one would be to take any of the models Nic has dealt with and show outcomes as a function of the parameters.

        You have no idea what constitutes “bias” and how to control for it since you don’t understand uncertainty analysis. Here’s some material that might help you understand selection and positive results bias.

        https://royalsocietypublishing.org/doi/full/10.1098/rsos.160384
        https://www.nature.com/news/registered-clinical-trials-make-positive-findings-vanish-1.18181
        https://www.nature.com/news/beware-the-creeping-cracks-of-bias-1.10600

        Your long and repetitious comment shows that you have no specific scientific criticism and misrepresent what Nic said and the implied uncertainty, which is large in this field.

        Science is in real trouble right now and independent people like Nic play a vital role in reform.

  35. https://onlinelibrary.wiley.com/doi/abs/10.1111/eci.13484

    New paper on effectiveness of stay at home orders and school closures. Lots of references too.

  36. Some genuine good news for the US. A home test kit which will tell you whether you have covid-19 within 15 mins of a nasal swab. It’s been approved by FDA and is in mass production phase. Mid 2021 is the expected release date:

  37. well herd immunity is an interesting concept..somewhat true..is it useful?
    On one end we must deal with contamination which is extremely complex with the consequence of high uncertainty on the other hand, we must assume that immunity is acquired.. the virus mutates..at a given pace and the population of the world changes a a given pace too…a virus epidemic may never “ends”
    so herd immunity concept is useful in cas of ” yearly” vaccination or to compute the number of cases to expect..
    it seems to me it only help to be able to be sure…this epidemic is over now..

    as climate change it is an interesting scientific question.. but we probably will not be able to answer it before having observed it.

  38. Nic: Looking at Tkachenko’s paper, I have serious questions about how accurately the heterogeneity parameter λ can be determined via plots of log Re(t)/R_0 vs S(t). Figures 3 and S3. S(t) is the susceptible fraction of the population at any time t. Unfortunately, testing misses essentially all asymptomatic infections and symptomatic infections that are mild enough that people aren’t tested. For NYC there is a linear relationship from 5% immune/95% susceptible to about 25% infected/75% susceptible. Do we have good seropositivity data at any point in the pandemic that allows us to correct for missed infections? It isn’t clear to me what period the data is from. Today’s data (NYT website for NYC) shows that a cumulative 5.8% of the population has tested positive, and probably only 3% if the data in Figure 3 is exclusively from this spring. If correct, it seems that this analysis is assuming that about 9 cases were missed for every one detected by PCR assay. If we are only missing 4 cases for every detected case, λ would be around 2 and HIT would be a more reasonable 40-50%. I’ve commented on different posts why testing can’t possibly be missing 90% of cases and don’t want to debate that subject now.

    Instead I’d like to ask what happens if we look at the same plot where S(t) runs from 0 (everyone immune/no one susceptible) to 1 (no one immune/everyone susceptible). Perhaps up to half of the detected infections in NYC have come after the spring surge. In that case, we might be approaching 50% of the total population being immune. In that case, log Re(t)/R_0 must be very small. Today the rate of detection of new cases is 50% higher than it during the worst of the pandemic. It is hard to see how data points from today’s surge can possibly fit on this graph. (The most recent points (fewest susceptible fraction) were already bending upward. Intuitively, this graph is saying that today’s surge is “impossible” because we have already reached herd immunity. Indeed, for λ = 4, your table says herd immunity is reached between 70% and 75% susceptible for R_0 = 3-4 and the final size of the epidemic is 44% to 53%

    Or it would be impossible if R_0 weren’t allow to change. Despite its subscript zero, R_0 isn’t a constant. It changes with social distancing and mask wearing. And Re(t) is calculated from S, which is uncertain. Figure S2 shows what happens if R_0 changes with Google’s mobility data. In this graph, the vast majority of the reduction in transmission is explained by a change in R_0 and not the decreasing fraction of the population that is susceptible. There no longer is a nice linear relationship, but the overall slope could be closer to λ = 2.

    Finally, looking at Figure S3 for 11 states, we see that the data doesn’t fit theoretical expectations very well.

    In summary, I have no doubt that the most gregarious people in a pandemic will become immune faster that the less gregarious people and lower herd immunity. The same if there are significant differences in susceptibility. I just don’t think that we have reliable enough data tell if λ is 4 or 1.4.

    Respectfully, Frank

  39. Nic: The other thing I struggle with is a tangible interpretation for λs or λb (dispersion in number of contact/day and dispersion is biological susceptibility). Let’s start with λs. Let’s say the average person has 5 potentially infectious contacts per day. If dispersion means the standard deviation of the number of contacts per day is 1 (20% of the mean), then normally distributed dispersion would mean about 97% of people have between 3 and 7 contacts per day. What would that make λs? See Equation 12. Now I can ask the same question about a standard deviation that is 2 (40% of the mean), but asking about a standard deviation that is 60% of the mean begins to get absurd because we can’t have a negative number of contacts. So perhaps my question needs to be reformulated in terms of some other distribution, perhaps a log normal distribution. (Moving to a log scale makes things less tangible to me.) So the question is how do λs of 1, 2, 3 or 4 translate into a more tangible measure of dispersion. If you tell me that λs = 2 means the average person has 5 potentially infectious contacts per day and the 10th percentile has 0.5 contact per day and the 90th percentile has 50 contacts per day, then my intuition says your model has an unreasonable amount of dispersion. If you tell me that λs = 2 means the average person has 5 contacts per day and the 10th percentile has 2 contacts per day and the 90th percentile has 12.5 contacts per day, then my intuition says your model has a reasonable amount of dispersion.

    λb comes from a different formula. And λ = λs*λb. So one could perhaps get an intuitive understanding of λb = λs = 2 and a few other combinations.

    • Franktoo, I agree that Tkachenko’s estimates of λ for NYC and/or Chicago might be inaccurate, but they also obtain similar results from other data. Moreover, their estimate of the social CV being 1 and the biological CV being at least 1 were derived from data on respectively contact network properties and age-group differences, both of which are independent of the NYC and Chicago epidemic data.
      You say “Despite its subscript zero, R_0 isn’t a constant. It changes with social distancing and mask wearing.” Yes; changes in R_0 are a focus of my article. But seasonality appears also to be an important determinant of R_0 – maybe the most important one. Infections stayed low in the summer when people reduced social distancing (e.g. in Sweden, but also in other countries) but took off in the autumn and winter even with stronger social distancing.
      You might find this paper from the Max Planck Institute for the Physics of Complex Systems interesting: https://doi.org/10.1371/journal.pone.0239678 . They conclude that “quantifying the the effects of mitigation requires knowledge on the degree of heterogeneity in the population”. They suggest that the German data is more consistent with their heterogeneity parameter, alpha, being no more than 0.2, which corresponds to lamba = 6, if mitigation had been mild. However, they find that if mitigation had been strong then heterogeneity could have been much smaller. The confounding effect of mitigation is a key reason for my past concentration on Sweden, where mitigation has been less strong than in most other western countries.

      • Nic: Thanks for the thoughtful reply. You quote the Max Planck paper:
        “quantifying the the effects of mitigation requires knowledge on the degree of heterogeneity in the population”. However, the converse is also true: quantifying the the effects of heterogeneity in the population requires knowledge of the effectiveness of mitigation. It also requires knowledge of the ratio of detected cases to total cases (resulting in immunity), because the slowing of the pandemic is mostly caused by depletion of susceptible people and more-susceptible people. If you don’t include heterogeneity, then slowing of any pandemic well short of herd immunity is attributed changes in R_0. When you add a heterogeneity parameter, you can explain any reduction in new cases/deaths by whatever combination of changes in R_0 and heterogeneity you choose. And when you can have anywhere from 2 to 10 total cases per detected case (let’s call this P for PCR), you really have no idea of how fast the pool of seronegative people is diminishing. (I don’t see where the Max Planck paper discusses P, but it is obvious critical to any analysis.) Many combinations of P, changing R_0, and heterogeneity parameter can fit the spring wave of this pandemic. Isn’t that the conclusion we should draw from the graphs in Figure 6 and the quote you cite?

        The fall wave of this pandemic is now placing some limits on these parameters. So all papers that don’t include information from the fall surge are now obsolete! When we have a record number of current cases and cumulative detected infections approaching 15% in many places, it is difficult to believe that P might be above 3, except early in the pandemic when testing was limited. This is especially true if heterogeneity is an important factor in limiting the pandemic. As we have discussed, comparing the IFR in hard hit US states in the spring to the IFR the north central states hit hard only this fall suggests that P has decreased less than a factor of two. Germany’s CFR has only fallen a factor of slightly more than 2 and there must have been some improvement in treatment and shift to younger, more-survivable cases.

        Germany now has 2 million cumulative cases of COVID. If you look Figures 6b, 6e and 6h, you’ll see that only the scenario (6b) with strong mitigation and no heterogeneity allows cumulative infections in the absence of mitigation to rise above 1 million cases. If you look at Figure 6i (high heterogeneity, low mitigation), R_0 had returned to nearly 2 in July, which meant that the pandemic was being held in check mostly by the depletion of the most susceptible population, while mitigation was having minimal effect. The total number of cases in Germany has risen 8-fold since July. Either R_0 has risen dramatically, or the high heterogeneity scenarios are absurd.

        FWIW, There are some papers suggesting that seasonal flu occurs almost exclusively in the winter because infectious aerosols persist longer when the air is dry and more evaporation leaves lighter droplets. R_0 increases in the winter, a seasonal epidemic grows until it is limited by approaching herd immunity in March and soon after warmer temperature make indoor air more humid. If so, the same factor likely caused the fall surge in COVID which hit the coldest US states the earliest. I’m no longer satisfied with cloth mask that blocks droplets, but lets most aerosol through.

      • Frank –

        I mentioned this before.

        > And when you can have anywhere from 2 to 10 total cases per detected case

        We have 23 million identified cases in the US. Let’s say P is at the low end of your range. That would mean a population infection rate of 14% and an IFR of 0.85% as it stands right now. Of course, we’d have to add more fatalities into that because if we kept the # of infections the same there would be another month or so of deaths, so let’s maybe say another 30 days of around 1500 deaths per day averaged over that entire period? Which would bring the IFR up to about 0.94%

        So that’s way higher than some of our friends are claiming. Almost 3 x as much.

        Let’s look at the other end. Let’s say that P is 8. That would bring the IFR more in line with what some of our friends are claiming – about 0.23%.

        Hmmm, but that would mean that the population infection total would be about 57% right now. And the infection rate actually still increasing, but lets say the infection rate started dropping tomorrow and reached 0 in a couple of weeks….even then the population infection total would have to actually be a bit higher.

        But our friends say that herd immunity should kick in at maybe 15%.

        Seems to me that somethings gotta give.

      • Joshua: I suspect we agree, but are approaching the problem from different directions. I think it is absurd for COVID and influenza to have similarly low IFR’s, as many concluded after the early seropositivity surveys reported about 10 undetected cases for every one detected case. (That high ratio could have been an artifact from a false positive rate of a few percent in the field.) This led to the early conclusion that COVID and influenza had similar IFRs. Both are transmitted by the same combination of mechanism and now show clear signs of the same seasonality.

        Given that COVID has been killing as many Americans in an average month as influenza does in the average year, an IFR similar to influenza seems absurd. And this is happening despite our best efforts to suppress the pandemic – efforts we rarely make for influenza. Those efforts appear to be suppressing the seasonal influenza pandemic that would normally becoming apparent by now. And most of the COVID pandemic has occurred in months when influenza kills few Americans. We are only beginning to experience the lethality of COVID during the months when most influenza deaths take place. My guessestimate is that COVID is likely to kill about 50 times as many Americans in a year as influenza does during the average year. (We are already about 12 times as many.)

        Nevertheless, the above measures of deadliness do not translate directly into a high IFR: Deadliness is a function of transmissibility, lethality per infection and the effectiveness of measures to mitigate the pandemic. Deadliness merely suggests COVID should have an IFR significantly greater than influenza. I’ve commented previously about many places where the cumulative number of detected cases is approaching 15% and at least one where the pandemic is still raging. If there were more than 1-2 undetected cases for every detected one, approaching herd immunity would be playing a significant role in slowing transmission. So I don’t think the current CRF/IFR ratio is greater than 2-3. Early in the US pandemic (in the Northeast), the CFR was about two-fold greater than it is in Montana, and some of that 2-fold improvement comes from improve treatment and the lower age of those being infected (as the elderly living in the community protect themselves more effectively.)

        Epidemiology papers discussing the importance of heterogeneity are all based on observations made before the fall surge in the pandemic. Those papers showed a wide range in heterogeneity was consistent with the spring surge in some locations, but they didn’t rule out many possibilities. Those skeptical of mitigation naturally emphasized the possibility that heterogeneity and approaching herd immunity were playing a role much sooner than expected. Assuming we are right, there will be definitive papers modeling both the spring and fall wavers showing that heterogeneity must be playing a less important role. However, given human nature, the papers refining how big (or small) a role heterogeneity plays will probably appear more slowly than the exciting papers offering hope of earlier herd immunity. In the meantime, it’s not surprising to me that our disorganized handwaving arguments are not as compelling as we think they should be to others invested in heterogeneity. The best I can do is draw attention to data I find important and see if others eventually agree or poke holes in my arguments.

        Most of all, I don’t want to erect a straw man by saying our friends claimed herd immunity will kick in at 15%, when they probably said or meant to say, that X, Y and Z suggest that herd immunity could be as low as 15%.

      • Frank –

        > Most of all, I don’t want to erect a straw man by saying our friends claimed herd immunity will kick in at 15%, when they probably said or meant to say, that X, Y and Z suggest that herd immunity could be as low as 15%.

        That’s fair. I won’t pretend that I can sometimes be prone to stretching what people say into the shape of straw men. Still, people should be careful to highly uncertainties and to foreground caveats. And they should be quick to reinterpret their analyses given that facts change on the ground. Nic has done all of that poorly, IMO, in this situation. Consider that a US Senator referred to Nic’s work to support a firmly stated conclusion that NYC had reached “herd immunity” and that the country should implement policies accordingly.

        That doesn’t mean that Nic shouldn’t be building toy models or that his doing so doesn’t make a contribution to the science. And it certainly doesn’t mean that he shouldn’t advocate for the policies he favors.

      • Frank –

        I assume you’ve seen this…but as for seasonality.

        I think there’s a reason that it seems many people who study this a lot are reluctant to weigh on on the strength of the signal of seasonality for COVID.

      • Josh: Yes, I know about Manaus and have mentioned it. However, I can find prisons in the US where the pandemic has infected more than 80% of the population and a ski/party town in Tyrol that was evacuated/isolated a few weeks into pandemic in Northern Italy. More than 40% of locals were seropositive in spring and returning skier spread the pandemic through Europe. Are the conditions in any of these places relevant to the US and Europe? Clearly there are some places where the disease in innately more transmissible and the HIT will be higher. To be rigorous, the first thing I like to know is whether R_0 (or just the doubling time) early in the pandemic in Manaus similar to what hard hit areas in the US experienced before mitigation began. Then I could be more confident that the lessons from Manaus should be applied elsewhere. (My instinct says yes.) However, I suspect Manaus probably lacked adequate testing and deaths will need to be used to determine the time course of the pandemic. I’d love to find a paper that discusses whether Manaus is an appropriate model for the US and Europe.

  40. UK-Weather Lass

    Here is a piece on SARS-CoV-2 written by Dr Irina Metzler FRHistS, medical historian and former lecturer at the University of Swansea, as well as a Wellcome Trust University Award Fellow, which covers a lot of stuff you may not have read elsewhere especially focusing on what maintaining good health in children and adults means in the real world and why we may be seeing this epidemic run rife in the UK etc.

    https://lockdownsceptics.org/covid-hyper-medicalisation-and-virus-interference/

    • Joe - the non epidemiologist

      From UK – Weather’s linked article –
      “The hygiene hypothesis is well known and argues that the more ‘clean’ we have become, the less chance our immune systems have had to be ‘trained’ in how to ward off pathogens. Hyper-hygienic conditions, which have been advocated in most high-income countries, through things as basic as using anti-bacterial products for everything from chopping boards for food preparation to the now ubiquitous hand sanitisers, have in fact contributed to the lack of training in childhood for most Westerners’ immune systems.”

      This points out one of the long term dangers from the lockdown/mask police / mitigation proponents. They are essentially advocating that the human race evolve to where humans can only survive in a sterile environment

  41. FWIW, new orleans had the lowest positivity rate until just recently in the state of louisiana (during, this, our third wave)…

  42. Thanks Nik,

    I’m going to drop this here intending to circle back and comment in a reply.

    Sadly Brazil has given us solid epidemiological data on SARS2 spread absent mitigation/suppression: Attack rate on the order of 75%

    https://science.sciencemag.org/content/early/2020/12/07/science.abe9728

    Covid-19 is an immunopathological disease (think AIDS). Seropositivity can not be factored into the SEIR model as presented. An adaptive immune response results in severe disease. Recall that Trump was hypoxic, in trouble, and seronegative at the time of administration of the Regeneron mAb.

    Most infections are neutralized my mucosal immunoglobulin.

  43. UK-Weather Lass

    This is a really interesting video about the UK variants. It is an exchange between a virologist and an epidemiologist about the possibility of the CoV-2 variants being more or less transmissible that the founder and what is needed to work the answer out. It covers work by Amy O’Toole in Edinburgh with official data and her observations of what is going on and the problems with other experts jumping the gun.

    The video is produced by Vincent Racaniello who tirelessly brings to us good quality science (search for him on YouTube) and has done throughout the pandemic. Frankly speaking it is far more enlightening and heartening to watch (these guys give you no BS) than the daily Downing Street Saga which may be running short of believable anything.

  44. https://www.cnbc.com/2021/01/13/moderna-ceo-says-the-world-will-have-to-live-with-the-coronavirus-forever.html

    Those who fanticize about ‘crushing’ the virus need to read this. COVID-19 will join influenza as a yearly epidemic with new strains. It will require new vaccines at least every year.

    • Moderna sell vaccines – and he’s right for now but I hear the massive R&D they did for COVID has lead to incredible discoveries in bioscience that will extend far beyond the current pandemic. Yesterday Moderna announced is developing three new mRNA based vaccines for seasonal flu, hiv and nipah virus.
      Check out AlphaFold and their breakthrough with molecular protein folding or these guys who seem close to isolating the cause of so many COVID symptoms.
      “The Odd Structure of ORF8: Scientists Map the Coronavirus Protein Linked to Immune Evasion and Disease Severity”
      https://newscenter.lbl.gov/2021/01/12/odd-structure-of-orf8/
      Roomers are that a cure for

  45. Matthew R Marler

    Nic Lewis, I respect your attempts to model the disease, but I think the net effect of these essays is to show that the model is too simple to be accurate, and information on the necessary entities (cf Ockham’s Razor) is limited.

    • Yes Matt. Nic has done a good job of analysis but modeling of epidemics seems to me to be an ill-posed problem and most predictions will turn out to be wrong to some degree or another.

    • Matthew,

      I don’t think complexity of the model is not the issue.

      Others have used much simpler models with reliable results.

      Here’s one from a climate scientist…

      https://bskiesresearch.wordpress.com/2020/04/14/model-calibration-nowcasting-and-operational-prediction-of-the-covid-19-pandemic/

      The issue, I think, is more generating an extraordinary result from a model and not questioning its veracity. Virtually nobody else believed herd immunity was in action. A model from anyone naive to the field generating such a result should always drive questioning the model first, rather than the whole of the rest of the field.

      • No one did a good job with predicting the second wave because the models cannot predict things like new strains, or seasonal effects or mitigation on R. There is not a lot of good science yet. We can’t even predict influenza with any accuracy. Each year the severity is significantly different.

        For an ill-posed problem modeling will provide only very limited predictive skill. This is well known to anyone with any mathematical training.

        It is common for laymen to believe the biased literature which has a strong positive results bias.

      • Matthew,

        2nd wave depends on lifting restrictions, which was not knowable.

        The level of prevalence in the uk was nowhere near herd immunity at the time. Nor in Sweden, obviously.

      • VTG, You are so obviously wrong. The second wave is just as severe in California and New York as Texas and Florida. California has had a tightening of an already extreme lockdown as has New York. Texas and Florida have only very light restrictions. Lifting restrictions had nothing to do with it.

        Most viruses are strongly seasonal including influenza.

      • dpy,

        A link to the views of mine you’re quoting on seasonality would be lovely.

        Thank you!

      • Compare and contrast:

        > There is not a lot of good science yet.

        and

        > VTG, You are so obviously wrong.

        The causality behind the efficacy of interventions is obviously, extremely complicated. As Frieden says:

        I”d love to see a comprehensive treatment of this issue.

      • Matthew R Marler

        verytallguy: 2nd wave depends on lifting restrictions, which was not knowable.

        If you look at the experiences of New York, Florida, Texas, and California, I don’t think you can sustain a claim that the 2nd wave resulted from changes in the lockdown orders.

        When a forecast depends on knowing something that has not occurred yet, a partial remedy is to forecast several evolutions of that something, and make the forecast for each such evolution. Climate modelers have done this plenty.

      • Matt, The problem with ensembles here is that the problem is ill-posed so the range of outcomes is huge.

    • I think the primary problem with modeling is that humans react to the virus and that changes the parameters in unpredictable and difficult to measure ways.

      In late February I wrote my own SEIR model just to get an intuitive feel for what might be coming. The model produced the same results, roughly, as Ferguson’s, except mine has only 100-200 lines of Python code. A

      nd, the results turned out to not be predictive at all, because of changes in behavior. Those changes are both the result of government, and very importantly, individuals acting on whatever information they get. In the age of the Internet, far more information, including good information and bad, is available than governments put out (I’m talking about intentional hiding), and people react to it.

      That means that Rt (or Re as some use) deviates radically from R0, and does so in ways hard to predict. If Rt remained at R0, my model would have been pretty close – it’s basic epidemiological laws, and it showed a Farr’s Law shape, of course.

      Here in Arizona, we were late to put in mitigations in the Spring, but well before mitigations, many of us were sharply curtailing interactions with those outside our household.

      If we look at the whole history of our three waves (the third currently at record levels), we see:

      First wave… no mitigation until late, then pretty strong mitigation, including stay-at-home orders (with significant exceptions) and closure of all but “essential” stores and jobs. The wave peaked and only subsided a bit.

      Then, as the temperatures of our normally very hot summer season came, we had a record peak for the US. – even though it was too hot to have much in the way of outdoor activity, even after dark.

      Not long before the peak, the governor finally allowed localities to impost mask mandates for the first time, resulting in 90% of the population being under those. He also closed or limited some high risk businesses. The wave subsided dramatically, with the Rt dropping to around 0.7.

      Then, as our winter arrived, and with it, very comfortable temperatures for outdoor activities, our third wave came, and has yet to end, although it might have leveled off. Government measures hardly changed at all and still have not, even after repeated requests from medical officials.

      The cause of the rise? It’s hard to say. Schools opened, but we keep being told that schools are not a major source of spread. The holidays arrived, around the same time as our “snowbirds” – people, mostly retired, who winter in Arizona and spend the rest of the year in northern climes.

      It would be hard for a model to deal with all of this, because the behavior is so hard to anticipate. The transmission of the disease is far easier to model than the behavior of the people.

      It would also be hard to use modeling to deduce much about the underlying factors – heterogeneity, mobility, immunity , given this complexity. And that’s my point – models work, but only if you know what to feed them, and variable human behavior defeats that here.

      For the “lockdowns don’t work” folks. That’s nonsense, but they only work if they are enforced. And, of course, they imposed huge costs, but that’s a different issue from “don’t work.”

      Personally, I would like to see some stronger measures here. At a minimum, something to reduce the mobility – I see way more cars on the road than at any previous time in the pandemic, even when our rate of cases is twice that of our previous (July) peak, and our hospitals are stressed to the point that the largest chain banned all non-emergency procedures starting the first of this year.

      • For me the problem here is that the evidence that stricter measures work is not there. There are studies showing a variety of results. Strict measures of course impact the most vulnerable most strongly. Upper middle class people can still pay the bills. Most people when they get laid off cannot. Giant deficit spending to subsidize most people is not possible without very serious long term economic harm.

      • “For me the problem here is that the evidence that stricter measures work is not there.”

        It depends on what you mean. They work well in China. They work in Australia – so far. And based on what is known about the mechanics of respiratory virus spread, they should work>

        This issue is how strict, which measures, how much compliance you get, and very importantly, whether stricter measures actually increase compliance, especially given that a lot of people are acting based on information other than the little that comes from government.

        So yes, they work. You raise the issue of the cost of them, and that’s a valid issue, but it doesn’t affect whether they work, but rather whether they are worth it.

      • “The problem here” is that people reach simplistic conclusions about the differential impact of interventions based on lazy assumptions about counterfactuals related to what would have been had things been different.

        > Strict measures of course impact the most vulnerable most strongly.

        The pandemic of course impacts the most vulnerable most strongly. And a lack of interventions would affect the vulnerable most strongly.

        The beautiful irony here is that when I posted about how Covid was having a differential impact on minorities you say that was “irrelevant.”

        > Most people when they get laid off cannot.

        Without the interventions, it is quite possible that people would have been laid off anyway without extended unemployment benefits and without stimulus checks. People would have been forced to decide in many cases whether to protect their health or stay home from work and get fired.

        There is no free lunch.

        > Giant deficit spending to subsidize most people is not possible without very serious long term economic harm.

        There’s plenty of evidence to suggest that in the long run, the best way to mitigate the economic impact to the poor and everyone else would have been to have a more robust federal assistance approach. And the return of investment on something like a massive rollout of antigen tests could have been a massive net positive.

        There is no free lunch.

      • Joe - the non epidemiologist

        messo comment – ‘It depends on what you mean. They work well in China. They work in Australia – so far. And based on what is known about the mechanics of respiratory virus spread, they should work”

        The mitigation steps worked well in china, Australia , NZ , SK, etc because they were able to stop it early before the virus infiltrated the general population, Once the virus became embetted in the general population, mitigation steps become far less effective

      • “he mitigation steps worked well in china, Australia , NZ , SK, etc because they were able to stop it early before the virus infiltrated the general population, Once the virus became embetted in the general population, mitigation steps become far less effective”

        What becomes less effective is test and trace mitigation. The other mitigations become more, not less important, and their effectiveness doesn’t change.

        Also, in China, they had a very high case rate in Wuhan, and they stopped it quickly and thoroughly through very strong lockdowns.

        I had hoped that in the US, the spring lockdowns would bring it down enough to do test and trace. Unfortunately, we didn’t have adequate testing capacity when needed, and also tracing doesn’t work as well here due to resistance, and to privacy concerns.

      • I cited earlier a study in European countries finding no benefit from stronger lockdown measures. Europe is probably a better match to the US than Australia. China is not very comparable as they have a strongly authoritarian government and a population that has a culture of compliance.

      • > due to resistance, and to privacy concerns.

        And cynical exploitation of the testing situation for the sake of political expediency. As in “Everyone who wants a test can get a test.” and out testing program is “beautiful.” and “The only reason we have soany cases is that we’re testing so much.” and arguments by political leaders that we’d be better off with less testing.

        Of course, political leaders who supposedly deserve so much credit because they supposedly accomplished so much are entirely devoid of any responsibility for our failures in testing despite that they are the head of the government.

  46. On R0:

    The more thought I give to the concept of R0 the less useful it appears.

    In fact, the number of individuals who’re on average infected by someone with the disease is virtually impossible to determine.

    1. In the early stages of this epidemic, testing is scarce and so case numbers were dramatic underestimates.
    2. Different countries or even different counties within a State will have dramatically different transmission rates.
    3. Most viruses are strongly seasonal. Thus do we want to say that R0 can vary by a factor of 4 depending on which month the epidemic started in? Of course, its impossible to determine exactly when an epidemic started because initial growth is very small.

    So, you can average over all seasons, all countries, etc. and get a number that will be incredibly difficult or impossible to compute accurately. This number is useless for predicting anything at a local or even country level where it’s actually important.

    To be fair most epidemiologists know this and don’t really care much about R0. It seems to be mostly used as a communication tool to convince people epidemiology is a mature science that can predict things. The papers Nic has discussed here show that much more sophisticated models offer better results.

    • So, now you’ve had time to think, how many times has the uk reached herd immunity so far?

      Once, twice, three times, more, or never?

      • This is a question I will leave as an exercise for you to answer using simple math software.

      • Well, at least you’ve realised just how ridiculous your argument is.

      • I suggest you do a refresher on reading comprehension. My argument is correct. R0 is not a magic universal constant for each new virus. In fact it is virtually impossible to compute and useless for predicting anything. R varies hugely depending on many things.

      • “I suggest you do a refresher on reading comprehension. My argument is correct. R0 is not a magic universal constant for each new virus. In fact it is virtually impossible to compute and useless for predicting anything. R varies hugely depending on many things.”

        R0 is the closest simple approximation we have for the inherent transmissibility of a new virus in humans. It is a constant, not exactly a universal one.

        R0 is what we would expect to see if an epidemic happened in an immunity immunilogically naive population without mitigation. Of course, there can be some different R0 values for different life styles of a population, but that’s about it. For a given population, R0 will only change through genome change in the virus (mutation, recombination).

        R (Rt seems to be the most common notation to this non-expert) is a time varying transmissibility value, with the same meaning (of transmissibility) as R0. Rt varies based on many things, including: ratio of susceptible to total population; mitigation; travel changes. Rt is *not* R0. You can use it in the same formulas and models as R0, but it is a time varying function of R0.

        R0 has a 0 in it for obvious reasons: the 0 time of the epidemic. That’s normal scientific notation for all sorts of parameters in many fields.

      • Dpy,

        How many times has the uk been in and out of herd immunity so far?

        Don’t be embarrassed, just inform us. We wait your wisdom.

      • I told you how to do the analysis yourself. I generally don’t waste time on unimportant questions that are being used as a disingenuous rhetorical device. Nonscientist anonymous activists often do that on blogs.

        Many places saw R drop below 1 this summer. The UK was one of them. You must use smoothed data to determine anything. The data is very noisy.

      • It’s not a difficult question dpy.

        How many times, according to you, has the uk been in herd immunity this pandemic?

        It’s almost as though you’re not answering because your positing is indefensible.

      • You didn’t read what I wrote or your reading skills need a refresh. I said the UK crossed a herd immunity threshold this summer. As winter set in R increased a lot and the herd immunity threshold increased.

        Kwok ( who I linked above) said that Hit varied from 5% to I think 78% or so on a country by country basis. On a regional or city basis it’s bound to be greater.

        The question for you is why you continue with rhetorical and silly questions to which the answer is pretty clear given a moments thought. You don’t seem to be willing to try to advance the discussion.

      • Meso, Some people do define herd immunity as 1 – 1/R0. Others use other definitions. My argument is that using R0 is worthless for doing anything. We know that R will vary a lot depending on the season. So this definition using R0 gives a number that is strongly dependent on which month an epidemic starts in!!! At the very least you need to average R over the coarse of a year, but there is the effect of the response and its effect on R.

        It’s just a silly definition. It makes nonscientists think they have learned something when what they have learned has little scientific value.

      • ” My argument is that using R0 is worthless for doing anything.”

        That’s simply nonsense. Scientists understand R0 and its limitations. I understand R0 and its limitations. You appear to believe that we don’t – that we treat it as a magic talisman or something. COVID19 is hardly the first epidemic for which this stuff has been used.

        And, for the population for which R0 is derived, the HIT without heterogeneity is in fact 1 – 1/R0. That’s a simple fact and can be proven mathematically. Heterogenity(lambda) changes that, as has been discussed, but also in a deterministic way that can be calculated.

        But when people say that a population reached herd immunity at some level, and then sometime later the same disease reaches higher levels, they were simply wrong (unless the pathogen changes). It’s that simple.

        Herd immunity is when the herd won’t sustain an outbreak, under conditions that are the same (other than immunity) as when the R0 value (and lama, and any other decorations) was derived. That’s a very useful definition.

        Mathematically, one models epidemics using R0 and factors that modify it. Those models work – if no mitigations take place. If you can predict the impact of mitigations and the level of them, the models will again work. If the weather changes and that modifies transmissibility (for example, increases aerosolization due to low humidity), that can again be factored in.

        You end up with an R(t) (usually written Rt) – R0* [set of factors].

        It is true that if you hold all of that constant, you can calculate that the outbreak *under those conditions* will peak at 1 – 1/Rt (and with lambda tossed in if you know it). But that is not herd immunity, and it confuses people to refer to it as such.

        That false definition has been used to argue against mitigations. While there are arguments to be made against mitigations, the “we reached herd immunity at only 10% of the population” argument (when R0 and lambda make it much larger), are bogus. Likewise, arguments that “we must have reached herd immunity because the epidemic is receding” are bogus, but often used. People look at the curve, notice that it fits the shape of a herd immunity induced curve, and draw the wrong conclusion (unless there was no change in conditions from the R0 condition, in which case herd immunity was reached).

        Of course, in an emergent disease situation, we don’t know a lot of this. We don’t know R0 accurately. We don’t have numerical values for the effects of specific mitigations. Even more difficult, is that we cannot predict behavior changes as a result of either government mandates, or independent decisions resulting from knowledge of the epidemic.

      • Yes, You just made the case that defining herd immunity using R0 has no practical implications. It is very possible to have reached the R0 herd immunity threshold but for the epidemic to keep growing for a very long time. That can happen if R keeps increasing as it might in winter time for example.

        BTW, all viruses will mutate over time and develop new strains with different infectiousness. So which one should be used to define R0?

        It’s a simple minded definition that tells us nothing either about how an epidemic will progress or for comparing various pathogens.

      • “Yes, You just made the case that defining herd immunity using R0 has no practical implications. It is very possible to have reached the R0 herd immunity threshold but for the epidemic to keep growing for a very long time. That can happen if R keeps increasing as it might in winter time for example.

        BTW, all viruses will mutate over time and develop new strains with different infectiousness. So which one should be used to define R0?

        It’s a simple minded definition that tells us nothing either about how an epidemic will progress or for comparing various pathogens.”

        Your approach is to throw out the baby with the bathwater. You keep asserting R0 is worthless, and yet I have yet to see an epidemiologist say that.

        You are correct that R0 might be different for winter than summer, although it probably doesn’t change much. Can you explain the mechanisms that would cause it to be different?

        When viruses mutate in a way that significantly alters transmissibility, then R0 has to be re-estimated for that variant. So far, in this epidemic, that has only occurred once (more than one strain with the trait, both appeared recently). So, you adjust R0 up, of course – it’s a different pathogen.

        But it is not a “simple minded definition” and it certainly does not “tells us nothing.” That’s an extreme statement.

        Science needs simplifications. It relies on them. But they have to be used knowing that.

        So, in epidemiology, it makes sense to look for a constant that defines transmissibility. That’s R0 (or for slightly more complex, R0 and a heterogeneity constant or two). It doesn’t mean that you stop at that point or that you assume you know everything. But it also doesn’t make it meaningless.

        Your argument like saying that Newtonian physics is useless because relativistic physics is more accurate.

      • Kwok says in theNature piece that the hit varies from 5% to 78%.

        The Newtonian physics example is very wrong. For velocities below say a million miles per hour, Newtonian physics is extremely accurate. The relativistic terms are less than machine precision on all computers.

        If you believe Kwok, using a single R0 will give predictions that fill the possibilities space. That’s a useless model because it can’t predict anything.

        It’s like trying to define with a single number the weather at a given place. You might get an average temperature over the year, but that is useless for predicting anything. Spokane and Seattle have similar average temps but totally different weather and climate. One is a maritime climate and the other is continental.

        Try to think of a quantitative prediction using R0 that is skillful.

        Any model that has a chance must try to model how R might change.

      • David –

        > Kwok says in theNature piece that the hit varies from 5% to 78%.

        And by your argument, with 0.1% (or less) of a population infected it could reach a “herd immunity threshold” as I illustrated in my example. And by definition Nic couldn’t be wrong no matter when he said they’d reached a HIT in Stockholm – no matter that they have an active pandemic now even though he said they’d crossed a HIT 8.minths ago.

        Once again, based on his calculations, and based on HIS DEFINITION of herd immunity, Nic projected another 1,000 deaths or so from COVID in Sweden. Since that point there at another 5,000 or so deaths (and about 450K of their 530K identified cases). IOW, his definition of herd immunity and associated projection was off by 500% on the more favorable for him metric of deaths. But according to your logic he wasn’t in error.

        You’re entitled to such a belief even if it is ridiculously. I prefer to think, however, that you know that it’s ridiculous but you’re just holding on desperately and trying to convince someone who isn’t actually evaluating your argument.

      • This is another meaningless response from you Josh. You can disagree with Kwok and deny science if you want. Nic was right about Sweden crossing a herd immunity threshold. Purely rhetorical sniping is childish and wastes your time.

  47. Mr. Lewis,
    If we use the formula you write about – what then is the theoretical ideal lockdown period?
    Or put another way: are there criteria which falsify the premise of the equation?
    Otherwise we’re all looking at a medical equivalent of the Drake’s equation – unprovable and thus useless.

  48. “Herd immunity by infection is not an option.”

    https://science.sciencemag.org/content/371/6526/230

  49. Seems that per capita would be a much more meaningful measures.

    Amazing that West Virginia, of all places, leads in that regard (doing considerably better than Texas, for example). I wonder why.

    • Texas is notable because it is a large state which makes logistics more complex. Yet the governor there has been able to get the vaccine injected rather than rejected.

    • “Seems per capita would be a much more meaningful measure.”

      Only to a fool.

      You have a limited number of vaccine doses provided to each State. The States responsibly is to get those doses administered as quickly as possible to appropriate individuals. Tracking per capita hides the poor performance of States.

      • Lol.

        > You have a limited number of vaccine doses provided to each State.

        So that’s a reason to go by the total number of vaccines given as opposed to the per capita rates? Which was my point – as a comparison of the two, not which single metric in and of itself would be best. Obviously, all these factors plus more factors should be considered.

        Looks like Texas does rank 6th in % of vaccines distributed. But not that much higher than a lot of other states. All things considered, that seems like a rather weak basis on which to make substantive comparisons.

        Given how so many conditions would vary by state, such as infrastructure available or the level of demand in the various states or the cost efficiencies of the rollout, or the rate of the distribution to the states – all of which I’m guessing you haven’t a clue about, it looks like yet another example of where people look at life and death matters and care most about scoring partisan points.

        Sad. But knock yourself out if it helps you to feel good about yourself.

      • Tracking percentage of unallocated vaccine doses hides the actual number of doses available but unadministered. States should be getting the vaccine administered asap. They should also track number of doses that go bad due to being beyond expiration date.

        Think. What is the purpose of metrics.

  50. lol. So do you feel the same way about NY, which is also a large state and where the per capita vaccination is roughly on par with Texas? ‘Cause personally, living in NY, I think the vaccination rollout has been terrible here.

    Or how about Alaska, a large state where the per capita vaccination is quite a bit higher?

    It’s really sad that you’re so focused on politicizing everything.

  51. Matthew R Marler

    Latest on lockdown orders:
    https://onlinelibrary.wiley.com/doi/10.1111/eci.13484

    A little dose of NPI is good; more NPI is no further improvement.

    • Matthew R Marler

      snippet: This analysis ties together observations about the possible effectiveness of NPIs with COVID-19 epidemic case growth changes that appear surprisingly similar despite wide variation in national policies.31–33 Our behavioral model of NPIs – that their effectiveness depends on individual behavior for which policies provide a noisy nudge – help explain why the degree of NPI restrictiveness does not seem to explain the decline in case growth rate. Data on individual behaviors such as visits to businesses, walking, or driving show dramatic declines days to weeks prior to the implementation of business closures and mandatory stay-at-home orders in our study countries, consistent with the behavioral mechanisms noted above.34–36 These observations are consistent with a model where the
      severity of the risk perceived by individuals was a stronger driver of anti-contagion behaviors than the specific nature of the NPIs. In other words, reductions in social activities that led to reduction in case growth were happening prior to implementation of mrNPIs because populations in affected countries were internalizing the impact of the pandemic in China, Italy, and New York, and noting a growing set of recommendations to reduce social contacts, all of which happened before mrNPIs. This may also explain the highly variable effect sizes of the same NPI in different countries. For example the effects of international travel bans were positive (unhelpful) in Germany and negative (beneficial) in the Netherlands (Figure 2).

    • Yes that makes common sense to me too. Degree of compliance I expect to decrease as measures get more strict and start to damage people’s livelihood. That may be what we are seeing in California. Strict measures are forcing large numbers of people to move out of “paradises” like California and New York to Texas, Florida, Tennessee, etc. For New York the permanent loses could be large. This can become a death spiral where declining tax revenue and growing expenses force ever more confiscatory taxation.

    • Matthew,

      As pointed out above, the paper equates Sweden and South Korea in their response (!)

      As with much of Ioannidis’ work on COVID, its interest lies more in why it is so eagerly shared outside the expert community than its actual content.

      Should you be interested in further analysis of the content, you could do worse than go here.

      https://mobile.twitter.com/GidMK/status/1349164532627693570

      • Matthew R Marler

        verytallguy: As pointed out above, the paper equates Sweden and South Korea in their response

        “Equate” is the wrong word here. There are no perfect comparisons, but those are nations that did not enforce strict lockdowns. Obviously the paper should not be “believed” before there are other supporting publications, but is there any evidence that the stricter lockdowns improve upon the benefits of the voluntary social distancing and less strict lockdowns?

        I do hope that the authors follow up this work with parallel analyses of data up through, say, Dec 2020.

      • “There are no perfect comparisons, but those are nations that did not enforce strict lockdowns. Obviously the paper should not be “believed” before there are other supporting publications, but is there any evidence that the stricter lockdowns improve upon the benefits of the voluntary social distancing and less strict lockdowns?”

        Any analysis that treats Korea and Sweden as similar in response is wrong.

        South Korea has had an intensive test and trace capability, and a citizenry willing to participate, to the extent that their cell phone mobile data is available in detail. I have not heard the same about Sweden.

        Also, South Korea had strong measures when outbreaks happened. Sweden had weaker measures.

      • Matthew R Marler
      • Matthew,

        Reply intended here is at the foot of the thread.

      • The problem here meso, is a deep confusion about statistics and the scientific method. Statistics is used to draw conclusions about general categories of things by randomly sampling that category. Thus you want to get as many dissimilar samples as you can and make them as random as possible.

        Absolutely nothing wrong with using as many countries as possible in a study of whether interventions work.

      • “The problem here meso, is a deep confusion about statistics and the scientific method. Statistics is used to draw conclusions about general categories of things by randomly sampling that category. Thus you want to get as many dissimilar samples as you can and make them as random as possible.”

        What does that have to do with my comment? If they treated the interventions in SK and Sweden as similar, they made a big mistake. If they didn’t, then the characterization I was responding to was incorrect. I pointed out the substantial differences between the two country.

      • Ioannidis et al absolutely did NOT treat them as “similar” what ever that means. They are two different data points in a dataset that is analyzed using statistics.

        VTG’s criticism is based on a fundamental confusion about statistics and the scientific method. Generally, you want as large a dataset as possible and one randomly includes a wide variety of cases.

  52. Further –

    Getting the vaccines out for COVID is an enormous and unprecedented task that was inevitably going to encounter massive obstacles. I see no particular reason why Pub politicians should earn more credit as group for getting vaccines out that Demz, but if after controlling for important variables it looks like they did to better I’ll happily give them credit for that.

    That said, it’s hard for me to believe we wouldn’t be able to do much better and save many lives and significantly limit economic damage had (1) we not starved our public health systems for decades and (2) Trump actually made vaccine distribution the highest of national priorities.

    Hopefully Biden will use National Defense Authorization. What should it be used for if not to save American lives?

    • Matthew R Marler

      Joshua: Hopefully Biden will use National Defense Authorization.

      Boy I hope not. The Constitution gives most power and authority for health and safety issues to the states, a feature of our governance respected by Trump.

      • There’s also nothing mutually exclusive about using the national defense act to address logistical issues and economic issues, and states having most authority and power over health and safety issues.

      • Good info on why some states performing better than others and sure, state-level autonomy looks important:

        https://www.cnn.com/2021/01/15/health/states-vaccinating-faster-west-virginia/index.html

        >Biden’s plan, while relying in part on federal resources, ultimately gives more power to states to accelerate their vaccine disbursement as quickly as they can.

        “We’ll fix the problem by encouraging states to allow more people to get vaccinated beyond health care workers and move through these groups as quickly as states think they can. That includes anyone 65 and older,” Biden said Friday.

        https://www.nbcnews.com/politics/white-house/biden-speak-covid-vaccinations-security-concerns-delay-inaugural-rehearsal-n1254381

      • Matthew:

        This is hilarious

        > The Constitution gives most power and authority for health and safety issues to the states, a feature of our governance respected by Trump.

        So under Trump its a good sign that he respects the Constitution. But under Biden it would be tyranny. Really, you boyz are hilarious.

        >> To help Pfizer, the deal calls for the government to invoke the Defense Production Act to give the company better access to roughly nine specialized products it needs to make the vaccine. One person familiar with the list said it included lipids, the oily molecules in which the genetic material that is used in both the Moderna and Pfizer vaccines is encased.

        >>> Under the leadership of President Trump, the Department of Health and Human Services leveraged the Defense Production Act (DPA) to apply priority rated orders for contracts with Becton Dickinson (BD)  and Quidel Corporation  through September

        https://www.hhs.gov/about/news/2020/08/20/trump-administration-uses-defense-production-act-to-aid-our-most-vulnerable.html

        >>>> In the early days of the COVID-19 pandemic, the Trump administration was criticized for not using the Defense Production Act quickly enough or in sufficient quantity to counter the spread of coronavirus. Now, after using the act 33 times since March 18, there is no denying that the administration is utilizing the act to fight this public health crisis.

        https://www.heritage.org/defense/commentary/administration-ramps-use-defense-production-act

      • Matthew:

        This is hilarious

        > The Constitution gives most power and authority for health and safety issues to the states, a feature of our governance respected by Trump.

        So under Trump its a good sign that he respects the Constitution. But under Biden it would be tyranny. Really, you boyz are hilarious.

        From the NYTimes

        >> To help Pfizer, the deal calls for the government to invoke the Defense Production Act to give the company better access to roughly nine specialized products it needs to make the vaccine. One person familiar with the list said it included lipids, the oily molecules in which the genetic material that is used in both the Moderna and Pfizer vaccines is encased.

      • From HHS

        >>> Under the leadership of President Trump, the Department of Health and Human Services leveraged the Defense Production Act (DPA) to apply priority rated orders for contracts with Becton Dickinson (BD) and Quidel Corporation through September

      • From HERITAGE.

        >>>> In the early days of the COVID-19 pandemic, the Trump administration was criticized for not using the Defense Production Act quickly enough or in sufficient quantity to counter the spread of coronavirus. Now, after using the act 33 times since March 18, there is no denying that the administration is utilizing the act to fight this public health crisis.

      • Matthew R Marler

        Joshua: From Heritage – used it 33 times since March 18:

        I stand corrected. Good comments

      • Matthew –

        Good response. So, what’s the conclusion – that Trump is a tyrant or that there’s good reason to use the Defense Protection Act?

        There is, of course, risk involved. But then again, there’s no free lunch.

      • Matthew R Marler

        Joshua: So, what’s the conclusion – that Trump is a tyrant or that there’s good reason to use the Defense Protection Act?

        I had been thinking of some of the harsher proposals that I had read, such as imposing and enforcing uniform lockdown standards across all the states, or enforcing uniform distribution standards for the vaccine. But his own speaking emphasized recognizing the authority of the states.

        Trump was not a tyrant, and the DPA can be used well or badly.

  53. Matthew R Marler

    Joshua: >Biden’s plan, while relying in part on federal resources, ultimately gives more power to states to accelerate their vaccine disbursement as quickly as they can.

    That’s yet to be tested, but hope springs eternal.

  54. South Korea and Sweden had approaches almost as different as it’s possible to be.

    To treat them as equivalent in a comparison is positively perverse.

    As far as the benefits and costs of more compulsory and voluntary measures is concerned I think it’s very hard to compare between cultures in different parts of the world.

    And until we are genuinely out of the pandemic we can’t know who actually had the best approaches.

    But so far, Sweden has done much worse, actually both in terms of more restrictions *and* more deaths than neighbours who locked down hard to start. South Korea has done far better than any Western country, again in both respects.

    • VTG –

      Re the latest Ioannidis paper (watch David duck).

      There’s an unsupported implicit assumption about direction of causality in all of this. There is a distinct possibility that the most stringent interventions take place where precipitating conditions are the worst. You obviously NEED to control for that to evaluate efficacy of interventions.

    • Oops –

      Didn’t see that you linked previously.

      Now we can watch David duck in two sub-threads!

    • Joshua,

      I read the paper before I saw the critique; it’s doing the rounds of the usual suspects.

      Putting SK and Sweden together is just risible.

    • This is all word salads with no substantive criticisms. The tweet just quotes from the paper as far as I can see. You of course can find support for any wrong headed point on Twitter.

      Just as an example claiming that Korea and Sweden are treated as equivalent is wrong and simple minded. They are 2 instances in a statistical analysis and not equivalent.

      • Just one substantive criticism is that there is a fundamental flaw in the analytical concept.

        They don’t control for the distinct possibility that the strongest interventions were implemented in the countries where the pandemic was the most virulent. Thus, there would be reasons to expect the outcomes to be worse in those countries independently of the degree of efficacy of those interventions. A lack of control for direction of cauality should explicitly be elaborated upon as a limitation. Was it?

      • The problem here is that VTG’s criticism and indeed yours Josh shows a deep ignorance of statistics and the scientific method.

        To show that “statins improve lipid profiles” you do a random sample with as many different people as possible and do a double blind experiment. Limiting your sample to people who are for example over 70 will lower the statistical power of your experiment and also make your conclusions much less useful in clinical practice. Further, there is a massive cost to doing a large number of studies of different “types” of people. There are an infinite number of possible “types” of people.

        What Ioannidis and company did is perfectly normal rigorous science.

      • Not controlling for, or even discussing the limitations of not controlling for, direction of cauality is not normal and rigorous epidemiological analysis to determine the efficacy of interventions.

      • This last comment takes the prize for drivel Josh. I doubt you have even read the paper. Normal science looks for associations. These then have implications for causation.

        “Implementing any NPIs was associated with significant reductions in case growth in 9 out of 10 study countries, including South Korea and Sweden that implemented only lrNPIs (Spain had a non-significant effect). After subtracting the epidemic and lrNPI effects, we find no clear, significant beneficial effect of mrNPIs on case growth in any country. In France, e.g., the effect of mrNPIs was +7% (95CI -5%-19%) when compared with Sweden, and +13% (-12%-38%) when compared with South Korea (positive means pro-contagion). The 95% confidence intervals excluded 30% declines in all 16 comparisons and 15% declines in 11/16 comparisons.”

        If the measures didn’t cause changes in the number of new cases as you seem to think should be “controlled for” (you have no idea even what that would look like), then they had no effect and the analysis is correct.

  55. Texas became the first state to administer 1 million COVID-19 vaccine doses. Gov. Greg Abbott (R-Texas) announced the milestone on Thursday, saying the Lone Star State is leading the nation in its vaccine program.

    Abbott said the state opened up vaccination hubs and expanded its vaccine eligibility.

    “As you know, we’ve now opened up an additional category and that is anybody over the age of 65 or older, or people under the age of 65 who face or deal with certain chronic healthcare conditions that have been pre-identified,” Abbott announced.

    https://www.oann.com/texas-becomes-first-state-to-administer-1m-vaccine-doses/

  56. In Italy 30,000 restaurants opened on the same day in defiance of covid lockdown rules which is destroying their businesses:

  57. dpy,

    So, when cases reduced in November, was that herd immunity threshold again, or something else?

    I think we’re done here.

  58. dpy,

    So, when cases reduced in November, was that herd immunity threshold again, or something else?

    I think we’re done here.

  59. I’ve been taking an interest in the UK data since December, before I was aware of Nics ideas on this, plotting my own graphs for some locations around the UK. I’m no expert, just a beginner.

    It feels like there is potentially a lot of very interesting, very useful information in there that needs to be thoroughly and urgently looked at. But it seems that every time I think I might see some conclusions newer data seems to throw that out of the window, and the vaccine is probably now going to further confuse any analysis or conclusions. I’m probably doing it all wrong. Presumably the modelers etc are doing all this anyway?

    I thought just before Christmas I was seeing the worst 2 or 3 places in south east England starting to fall due to a kind of herd immunity in the non adequately shielded group (because they appeared out of control despite lockdown) at around 11 to 13% cumulative on my adjusted numbers (see below, which are probably wrong and different from anyone elses, but maybe better than no adjustment), but then they took off again. Then I thought I was seeing them peaking due a kind of herd immunity at around 16 to 18% cumulative on my adjusted numbers. But they all peaked on 30th or 31st December which seems rather strange to me, also considering some other places are now also showing they peaked on those same dates, but much lower % and other UK places are still rising.

    Of the locations I’ve plotted, I think 4 have so far peaked at 16-18%, 3 at 12-13%, one at 7%, my numbers. Maybe some places are peaking due to a kind of herd immunity in the non adequately shielded group(?) and the value depending on whether more or less Christmas mixing occurred(?) in other places lockdowns are now effective? Did Christmas shopping etc and 25th cause a lot of mixing then immediately everyone was much more careful, plus being at home, not at work, etc so lockdown was finally effective(?), but wouldn’t we expect to see the result more like 10 days later not 5? On other hand my data could be out plus or minus several days. And wouldn’t we expect a lot more places to peak about that same date if it’s the lockdown?

    I think Liverpool is particularly interesting. I am perhaps wrongly assuming it has now peaked but it is showing a slight drop both in daily cases and the % of positive results for a few days now. Below are the 4 that I wonder if they have reached a sort of herd immunity; first number is cumulative % (my badly adjusted numbers) just before entering the November/December wave, 2nd is % at peak, then date of peak (UK format) –
    Swale (Kent, south east) – 3.6%, 15.7%, 31/12/20
    Medway (Kent) – 5.1%, 17.8%, 30/12/20
    Dartford (Kent) – 8.1%, 17.8%, 30/12/20
    Liverpool (North west) – 13.6%, 17.1%, 6/1/21 tbc

    I think Liverpool was in the news a lot in October and Swale in the news a lot in December as one of the first badly affected by the new variant and out of control. No doubt Liverpool will prove me wrong in a few days by not peaking yet.

    Rather than Nics lambda, or heterogeneity I’m just splitting the local population into a group A and B in my mind. I’m assuming group A are those not adequately shielded and assuming they represent most of the infections and are x% of the population and x changes value with lockdowns and behaviour, maybe e.g. lockdown1 x= 0.3 (30%), 0.8 over the summer and 0.5 now? If z is the % of A who need to have been infected to bring their R down to 1, (and changes with season) then I’m simplistically assuming the number of daily cases should start dropping when C = zx. Where C is cumulative infections as % of local population. So e.g. if x is 0.5 (50%) and z 0.4 I might see a peak in daily cases when cumulative value is 20%, say?

    My next thought was how do you adjust the data for cases to get to an actual value. I used a very simple, probably wrong method taking into account recorded cases, number of tests and percent of tests positive. I’m sure my simple equation is way off and needs a better method, it was just a quick 1st attempt to see if there was potentially anything interesting in the graphs. After adjusting, the first wave has gone up massively, I’m concerned its too much, but 1st and current waves now look better than the raw values in relation to the hospital admissions.

    • Paul: The number of cumulative cases detected by testing is a fraction of those becoming immune, because testing missed asymptomatic cases and mildly-symptomatic cases that don’t get tested. Surveys in the spring suggested that 6-15 people became immune for every infection that was detected by PCR. That ratio appears absurd today. Given what we know, 1 missed infection per detection infection seems like a reasonable, but “soft”, lower limit. The absence of unambiguous signs of a slowdown in due to approaching herd immune suggests to me that 2 missed cases for every detected case is reasonable upper limit.

      Around Thanksgiving, North and South Dakota were the hardest hit US states, with cumulative cases (essentially all in the fall) rising above 10%. ND is 12.6% today, but has the fewest current cases in the US (20/100,000). Some combination of restrictions, behavior changed by fear (the hospital are overflowing) and POSSIBLY approaching herd immunity have caused a dramatic reversal. In Burleigh County with the capitol of Bismarck, cumulative cases are nearly 15%. However, the conclusion that approaching herd immunity MUST BE playing an important role is suspect.

      Arizona is currently the worst state (119 cases/100,000/day, but only 9.2% cumulative testing positive). Two of its counties are proof that the pandemic can rage (134 cases/100,000/day) with 15+% cumulative cases. Things are bad along the Rio Grande where the pandemic hit hard in early summer: Webb county, TX: 12.4% cumulative cases, 203/100,000/day! Los Angeles and San Bernardino counties: 10.0% and 11.4%, 139 and 157 cases/100,000/day. These places are approaching herd immunity at a rate of about 1% of population testing positive per week.

      • Many thanks Franktoo, I think your numbers give some support to my adjustment results with my first wave going up massively and recent results being about twice the raw values. Also my daily numbers, adjusted seemed roughly in line with ONS (office for national statistics) estimates of 1 in 30 being currently infected a few weeks ago if I assume each person is infected for 7 to 10 days. And perhaps also not far out for cumulative values over the summer when I think I heard they thought about 1 in 20 had so far been infected.

        Although I suspect my equation (adjusted cases from recorded cases, number of tests, % of tests positive) is going way out at the extremes; considerably over adjusting when testing capacity very low and % of positive results very high and under adjusting when testing very high and % positive very low. Ideally I’d rewrite my equation and more accurately check/calibrate against estimates from other sources/methods on how many people have been infected etc.

        I was thinking along the lines of there should be some statistical/mathematical method to adjust the recorded cases taking into account also number of tests and % of tests positive. I’m thinking this is important to do so we can hopefully get useful and important information out of the data: Enable better comparison of old and recent waves. But even within waves I suspect changes in number of tests gives false trends, false peaks and troughs and with suitable adjustment of each days cases based on total recorded cases, number of tests, % of tests positive we can better study actual size of peaks and their dates?

        I was hoping my method might be useful in looking at whether a kind of herd immunity is forming in some places, effect of lockdowns, if immunity lasts from first wave, if that immunity works for the new variant, if the new variant is more transmissible.

        The first place I started watching was Swale, Kent as it was in the news as the highest numbers in the country for a long time and probably the first badly affected by the new variant. Numbers kept going up despite lockdown early November and it has been in lockdown ever since. It’s numbers stayed high and slowly rose for a long time, got to about 950/100k/week (unadjusted). My feeling at the time was it was out of control, government or local council not taking it seriously enough and hospitals coping because they could send people out to other areas which were yet to be affected. So Swale first sparked my idea that unless lockdown was considerably tightened up the only way it would stop would be when the non adequately isolated group reached a sort of herd immunity.

        Of the places I’ve plotted so far, Medway, next door to Swale was initially behind then overtook and got to 1168/100k/week, Brentwood in Essex got to 1562 (223/100k/day, I presume), unadjusted. So it felt like some of these places, looking out of control must only be peaking due to a kind of herd immunity as I couldn’t see enough in lockdown2 then tier 3 then 4 to bring it under control. But I have low confidence in that conclusion now looking at other places: Just been looking at a few more UK locations and so many places seem to have peaked on the same two days, 30 and 31 December and at lower cumulative % implying a lockdown effect? Or is it because the lower ones have managed to so far avoid the new variant?

  60. Just to recap since this “interaction” is replicated many many times on the internet including many of times here with the same suspects. There are two of these types who have a massive comment footprint on this post.

    Anonymous nonscientist sees something by a real scientist that is an interesting contribution but might harm his political narrative. Activist joins conversation.

    1. He quibbles about definitions, provides no evidence, and denies the citations of the scientist from other scientists.
    2. Since the activist is incompetent to do real science or math he is reduced to proof texting various pseudo-scientific sources such as that leading scientific journal, Twitter.
    3. He then goes on the attack a lion of the medical field with vague falsehoods and other rhetorical devices to cast doubt on an excellent body of work.
    4. Then he shows his utter ignorance of statistics by stating that doing a statistical analysis involving data from many different counties means the authors are saying they are equivalent.
    5. Activist ignores real scientific analysis of HIT, R0, R and whether these are useful concepts or not.
    6. Activist slinks away to read the Guardian and other top flight scientific journals.

    The sad part is that these activists are terrible merchants of doubt. Some people I guess don’t have more interesting things to pursue.

  61. The pandemic is winning:

    Biden goal: 100 million vaccinations (50 M people) in 3 months. That’s 0.55 million/day. Currently 0.24M/day test positive. We probably miss at least 1 and possibly 2 asymptomatic or mildly-symptomatic cases for every case we detect.

    Conclusion: The pandemic is currently immunizing Americans as fast or faster than the vaccine.

    What is the value in Operation Warp Speed when it is going to take longer to simply vaccinate Americans than it took to develop a vaccine!

    • Very good point. Many realists have been saying that herd immunity was our destination and anything beyond mild mitigation would not change the outcome much and be devastating in its human toll.

      The Humana CEO says covid 19 will be with humanity for a long time.

      Reports out of Norway indicate that quite a few older folks have died from complications after vaccination.

      Sometimes tragedy strikes and there is little we can do. The misleading idea that science can conquer anything (often propagated by scientists themselves) is quite wrong. The political narrative that if we followed the science we would have prevented hundreds of thousands of deaths is worse. It’s about as valid as burning “witches.”

      • “Many realists have been saying that herd immunity was our destination and anything beyond mild mitigation would not change the outcome much and be devastating in its human toll.”

        Okay, that is just plain nuts!

        First, we have vaccines. Those vaccines will save a lot of lives over, at this point, trying to achieve herd immunity through vaccination.

        Second, assume we did not have vaccines. We would have three choices:

        1) Let the infection run its course. Let’s assume that everyone just goes on and lives life as normal – which is which most herd immunity through infection advocates want. The rate of infection would rapidly get beyond the ability of the medical system to handle. The infection fatality rate would soar, plus even those not infected would die because of the destruction of the health care system (loss of capacity, and death and disability of providers). This happened in Tijuana, for example, and in other places. It would be catastrophic.

        But, it wouldn’t work that way. A lot of people would cut back their interactions with others as much as possible. They would stop traveling, stop eating in restaurants, stop going into stores, and wear masks. The result would be pretty much the same economic wreckage we see with lockdowns. Except, this wouldn’t be enough to keep the health care system working, so we’d still have a catastrophic failure, but at a lower level.

        This would continue until either stronger measures were used, or vaccines became available. And it would be a disaster.

        If you disagree, please respond to my points.

        2)Control the infection the way the Chinese and Taiwanese have, while waiting for better medical treatment, and beyond. It works, it does not require a vaccine, but it does require extremely good surveillance, and it requires *short term* lockdowns to stamp out outbreaks. It also has major impact on travel. It does not require totalitarianism – Taiwan is a democracy.

        3) Hold the course with mitigations until vaccines provide herd immunity.

        Please explain why, at this point, that point 3 is wrong.

        “The Humana CEO says covid 19 will be with humanity for a long time.”
        Your point?

        “Reports out of Norway indicate that quite a few older folks have died from complications after vaccination.”

        Evidence. We knew all along that people would die after getting vaccinated, because people die all the time. Some people, a small number in the US so far, have dangerous reactions. None so far have died. A few might over time. So what?

        “Sometimes tragedy strikes and there is little we can do. The misleading idea that science can conquer anything (often propagated by scientists themselves) is quite wrong.”

        That’s a complete straw man argument and has nothing to do with this debate.

        “The political narrative that if we followed the science we would have prevented hundreds of thousands of deaths is worse. It’s about as valid as burning “witches.”

        What BS!

      • Your evidence on vaccinations is out of date. Norway has attributed as I recall 7 deaths to vaccine reactions and is investigating 24. I will get vaccinated but it’s not risk free.

        There are by now a host of papers on lockdowns. One I cited earlier finds no statistically significant effect of harsh measures. At best there is not a conclusive consensus.

        You really didn’t respond to much else. You believe that we can suppress an epidemic with strong measures. As California, New York, Florida, Texas are very convincing evidence of the futility of strong lockdowns. They also show how strong measures can kill your economy. People are fleeing New York and California in droves. Businesses are leaving. Tesla is a big one.

        You cherry pick two countries as evidence. That’s not convincing. Virtually every European country is experiencing a strong second wave regardless of wide ranging levels of mitigations and cultures. Ditto in the US.

        Even Cuomo says they must open soon or there will be nothing to reopen.

      • Joe - the non epidemiologist

        Meso’s point #2
        “2)Control the infection the way the Chinese and Taiwanese have, while waiting for better medical treatment, and beyond. It works, it does not require a vaccine, but it does require extremely good surveillance, and it requires *short term* lockdowns to stamp out outbreaks. It also has major impact on travel. It does not require totalitarianism – Taiwan is a democracy.”

        The problem at this point, is that the virus has become to deeply embedded into the general population to make that approach even remotely feasible. The only time that approach could have been implemented effectively was in Jan/Feb which the political powers on both sides of the Aisle opposed.

      • “The problem at this point, is that the virus has become to deeply embedded into the general population to make that approach even remotely feasible. The only time that approach could have been implemented effectively was in Jan/Feb which the political powers on both sides of the Aisle opposed.”

        The Wuhan experience demonstrates that this is false. The virus was “deeply embedded” there, too. They whipped it.

      • joe -the non epidemologist

        meso – china whipped as you say because they had an extremely hard lockdown – they have a repressive country which allowed for an extremely hard lockdown. The other countries that did it taiwan , SK were able to do it before it became embedded.
        That type lockdown that china imposed would never have worked in the US

      • “That type lockdown that china imposed would never have worked in the US”

        Sadly, you are probably right – to our detriment. Their economy is far more open than ours and has been since they beat the virus down. They get outbreaks – there’s one right now near Beijing, but they beat them down too.

        The relatives and friends of the nearly 400,000 dead Americans probably wish we did have that lockdown. Done right, which is, as you say, harsh, the lockdown only lasts one to two months. After that, you have your life back with some exceptions: mask wear required, some social distancing, etc.

        But some free countries have done far better than the US. Taiwan, of course, did very well, but that’s because they caught it very early, and were ready because of SARS.

        South Korea has done pretty well too, although they have had bigger outbreaks, as has Japan. Both were, I think, more prepared because of SARS and MERS (which hit South Korea). These are free countries.

        http://91-divoc.com/pages/covid-visualization/?chart=countries-normalized&highlight=South%20Korea&show=highlight-only&y=both&scale=linear&data=cases-daily-7&data-source=jhu&xaxis=right&extra=Japan#countries-normalized

      • Also bear in mind that China’s data may be unreliable. Their press is constantly pushing propaganda.

      • “Also bear in mind that China’s data may be unreliable. Their press is constantly pushing propaganda.”

        China’s data is unreliable, but not so unreliable as to undermine the fact that they have done far better than the US, and are continuing to do far better.

        They have propaganda and are totalitarian, but a lot more information gets out of there than from the old USSR, or from China in Mao’s day.

      • https://virologyj.biomedcentral.com/articles/10.1186/1743-422X-5-29

        I think epidemiology I’d too immature for us to know if mitigation measures work. In any case the empirical evidence is that they don’t. We can’t get people to quit smoking and lose weight. Compliance in a free society is always going to be spotty

      • dpy

        I think epidemiology I’d too immature for us to know if mitigation measures work. In any case the empirical evidence is that they don’t.

        You see to be arguing that social distancing doesn’t mitigate flu outbreaks.

        Please tell me that I’ve misunderstood, otherwise that’s seriously insane, given what’s happened to flu prevalence through the COVID outbreak.

        https://www.who.int/teams/global-influenza-programme/surveillance-and-monitoring/influenza-updates/current-influenza-update

      • We don’t know the cause of flu ‘disappearing.’ We know very little about flu. Yet you show up here again with no science and spread falsehoods about science you are ignorant of.

        https://virologyj.biomedcentral.com/articles/10.1186/1743-422X-5-29

      • dpy

        We don’t know the cause of flu ‘disappearing.’

        Wow. Just wow.

        We institute worldwide social distancing policies to prevent the spread of an infectious respiratory disease.

        Influenza disappears at the same time, in a way which has never been seen before.

        Yet we don’t know the two are causally related.

        First of all herd immunity doesn’t require much, if any actual immunity, now this.

        Alright, I’m done. You’re way beyond any kind of rational discussion here.

      • I understand VTG who you don’t want to further embarrass yourself and expose your ignorance. Association does not mean causation.

        I’ve seen many possibilities discussed. Testing for flu may have gone to zero as people focus on covid 19 is one example.

        Your response to a link I posted to a peer reviewed paper on the flu and our deep ignorance is? Reading real science I guess takes you away from that leading scientific journal Twitter.

    • Frank –

      > What is the value in Operation Warp Speed when it is going to take longer to simply vaccinate Americans than it took to develop a vaccine!

      I don’t understand your point. The value is that in the end many fewer get infected and/or die than otherwise.

    • “Conclusion: The pandemic is currently immunizing Americans as fast or faster than the vaccine.

      What is the value in Operation Warp Speed when it is going to take longer to simply vaccinate Americans than it took to develop a vaccine!”

      It’s only winning because people are not taking adequate precautions – especially in the LA area and here in Arizona. How to get them to behave better? I’m not sure, but I don’t think we try hard enough.

      I’d have a big propaganda campaign to wake people up to it – like the one we had against cigarette smoking. Images of people on ventilators on signs all over the place. Frequent strong talks by popular politicians and media figures. That sort of thing.

  62. Monthly suicide rates in Japan increased by 16 percent from July to October 2020 compared to the same period the previous year, Japanese researchers have found. The three-month interval coincided with Japan’s second wave of Chinese coronavirus outbreaks, Reuters reported Sunday.

    https://www.breitbart.com/asia/2021/01/18/study-japan-suicides-jumped-16-percent-coronavirus-second-wave/

  63. Florida, which has been more open and less restricted than its west coast counterpart, has recorded just above 1.5 million cases. Yet California, despite being among the strictest in the nation when it comes to COVID lockdowns, has recorded nearly twice that figure since the start of the pandemic.

    https://www.foxnews.com/politics/closed-california-versus-open-florida

    • Joe - the non epidemiologist

      Jim – California total cases per 100k population is approx 7600 vs florida with approximately 7300.

      For all practical purposes, nearly identical rates. Though your point of vastly different levels of mitigation efforts and compliance with mitigation efforts is valid.

      At the individual level/micro level, mitigation efforts should greatly reduce the spread of Covid. However on the macro level, the positive effects of mitigation dont seem to be having any substantive effect.

      • CDC says that disparity between California and Florida is already big and getting much worse very quickly.
        In California the number of new cases in the last 7 days was 100.9 per 100k in California v 62 per 100k in Florida.
        California recorded 279,000 new cases in the last seven days. Florida 93,000. Covid is spreading three times as fast in California as Florida.

        https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days

        Plus Florida is actually administering the vaccine to people. The state has vaccinated more of its population than California and is still vaccinating at a higher rate.

      • Thanks for that Jeff. There is a growing body of such comparisons that make it harder and harder to argue that lockdowns add benefit over mild and mostly voluntary policies.

        https://virologyj.biomedcentral.com/articles/10.1186/1743-422X-5-29

        was pointed to by someone earlier. It’s really an eye opener on just how ignorant we are about viral epidemics.

      • Hi dpy6629,
        I think that when this is all said and done the verdict on lockdowns will be- 1. You can’t impose them in time. A highly contagious virus will spread a long way before anyone can act.
        2. You can’t actually lockdown a place like New York City. The city was 8 million people all living on top of each other coughing on each other. Then half of them fled.
        3. You can’t actually keep the entire young person population locked in their rooms for 10 months. Which means they’re going to catch it and give it to their parents, neighbors, local store clerks, etc etc. Especially once they realize the virus is harmless to them.
        4. It’s a bad idea to politicize a virus. The number of new cases in the last 7 days (per capita) is worse in New York than Florida just as it is worse in California than Florida. Absent storm troopers, any “lockdown” requires voluntary compliance and accurate information about how effective the effort is. The LA Times, The New York Times, The Miami Herald, and CNN/ABC/NBC/CBS are all “reporting” the opposite of the truth. They report that the Florida government and Floridians are doing it all wrong. And that New York officials and residents deserve awards for their extraordinary effort. In which state do you think people will be more careful tonight?

      • Good summary Jeff. It is indeed true that over the last 4 years (perhaps 12) American corporate media has descended to yellow journalism and is worse even than the Gilded Age. They are very sensationalistic. They are openly highly partisan and ideological. Their ideology is woke ideology which is based on lies such as the 1619 project. They demonize anyone who disagrees and try to cancel them. At least in the 19th Century there were few barriers to entry in the newspaper business and there were literally tens of thousands of papers to choose from with every possible point of view.

        Covid19 has indeed been weaponized by the media. They constantly focused on totally wrong case fatality rates up to 10% while ignoring more sober (and correct) scientists. They have portrayed Cuomo as a compassionate very competent leader and Trump and De Santos as causing death and suffering. They have stirred up unwarranted fear spread by TV “doctors” who are often unqualified. I don’t see how this can be fixed other than by breaking up into little pieces the media and tech titans.

      • Good summary Jeff. The corporate media have for at least 4 years descended into a level of corruption not seen since the Gilded Age. Extreme sensationalistic, open and extreme partisanship, and shaping every story to support ideologies that are based on lies. It’s actually worse now because in the 19th Century there were papers with a huge range of points of view. With the tech titans censoring what information most people see, it is similar to the Chinese government’s information operations.

        The only solution is to break them up into little bitty pieces.

      • > .It’s a bad idea to politicize a virus. The number of new cases in the last 7 days (per capita) is worse in New York than Florida just as it is worse in California than Florida.

        I’ll give you credit for the unintentiinal irony post if the day – where you completely ignore the relevance of rates of testing when you compare rated of identified infections across communities to draw conclusions a out the efficacy of interventions.

        But I will certainly agree that it’s a bad idea to politicize the virus. Comparing across communities is the easiest way to do that, however – because controlling for confounding variables, timing of interventions, levels of “compliance” etc. is nearly impossible and just opens the door for confirmation bias.

        Another example of why you shouldn’t make facile comparisons:

        -snip-
        New California Variant May Be Driving Virus Surge There, Study Suggests https://nyti.ms/39Khpqp

      • Josh, Another nonsense comment from you that is completely beside the point being made.

        Jeff was making the completely true observation that the media has been doing a politically biased and fraudulent hatchet job on De Santos and portraying Cuomo as a hero of the people.

        His comparison is not cherry picked. There is no obvious correlation between the political orientation of a state’s governor and their deaths per capita. Nor is there any obvious correlation with mitigation measures. Perhaps at some point, there will be a more rigorous analysis. For Europe, the best evidence shows pretty. much what I just said in that case.

      • David –

        Your opinion is noted. Along with your arguments that a vaccine wouldn’t alter the course of the pandemic. And your argument that the pandemic was over this summer. And that the summer spike wouldn’t lead to a spike in deaths. And your opinion that Nic wasn’t wrong when he sad that Stockholm crossed a HIT 8 months ago and that they would only have around 7400 deaths in Sweden and that 0.085% population fatality was likely a hard limit. And your silly errors about testing rates on Florida relative to NY. And your silly characterization of Andrew Gelman as a know-nothing. And your silly defense of the Santa Clara study… because Ioannidis is a “lion.”

        And your silly appeals to authority.

      • Drivel again. We’ve been over every one of your fraudulent out of context proof texting droppings. Hit was almost certainly reached in Sweden. I newer mentioned testing, that was Jeff. You are such a motivated ankle biter you can’t get any detail right. Grow up. Most people here feel the same way in case you are unable to read clearly.

  64. Also, about 21% of Florida population is over 65 vs about 15% for Cali.

  65. It’s worth quoting the abstract of the paper I linked above for VTG for the rest of the denizens, I know VTG will not read it.

    “The epidemiology of influenza swarms with incongruities, incongruities exhaustively detailed by the late British epidemiologist, Edgar Hope-Simpson. He was the first to propose a parsimonious theory explaining why influenza is, as Gregg said, “seemingly unmindful of traditional infectious disease behavioral patterns.” Recent discoveries indicate vitamin D upregulates the endogenous antibiotics of innate immunity and suggest that the incongruities explored by Hope-Simpson may be secondary to the epidemiology of vitamin D deficiency. We identify – and attempt to explain – nine influenza conundrums: (1) Why is influenza both seasonal and ubiquitous and where is the virus between epidemics? (2) Why are the epidemics so explosive? (3) Why do they end so abruptly? (4) What explains the frequent coincidental timing of epidemics in countries of similar latitude? (5) Why is the serial interval obscure? (6) Why is the secondary attack rate so low? (7) Why did epidemics in previous ages spread so rapidly, despite the lack of modern transport? (8) Why does experimental inoculation of seronegative humans fail to cause illness in all the volunteers? (9) Why has influenza mortality of the aged not declined as their vaccination rates increased? We review recent discoveries about vitamin D’s effects on innate immunity, human studies attempting sick-to-well transmission, naturalistic reports of human transmission, studies of serial interval, secondary attack rates, and relevant animal studies. We hypothesize that two factors explain the nine conundrums: vitamin D’s seasonal and population effects on innate immunity, and the presence of a subpopulation of “good infectors.” If true, our revision of Edgar Hope-Simpson’s theory has profound implications for the prevention of influenza.”

  66. Article on new study: “College Campuses Are COVID-19 Superspreaders, Study Suggests”

    “Senior author, Ellen Kuhl, adds: “Strikingly, these local campus outbreaks rapidly spread across the entire county and triggered a peak in new infections in neighboring communities in more than half of the cases.”

    https://www.labmanager.com/news/college-campuses-are-covid-19-superspreaders-study-suggests-24876

  67. As Sweden has been so much in the news and has been the subject of 2 articles here, I provide this link which gives comprehensive data from Swedish sources about 2020

    https://softwaredevelopmentperestroika.wordpress.com/

    The take home is that the excess deaths in 2020 in Sweden were the worst since 2012 when taking into account population size. In the UK every year prior to 2008 to 1900 had worse excess death rates than 2020 when taking the same factors into account. There are many factors to take into account in these bald figures.

    Both death rates in Sweden and the UK made worse by the decision to send infected or non tested patients from hospitals into care homes and not protecting the hospitals which in themselves became vectors of infection.

    In Britain up to 30% of patients being infected who came in for other reasons. It is currently 12% and rising again, around 40% of all UK covid deaths occurred in our care homes and although it dropped late last year is rising again as the same policies are provided, as the NHs try to clear the decks to provide more beds

    It would be interesting to see this data picked apart by NIc as no one wants to pretend covid is a hoax and that it is not a serious ailment, with those affected ranging from not knowing they had it to death and every condition in between.

    However that is one part of the balance sheet, the ongoing consequences of ill health through not getting treatments,(some 4 million treatments are in abeyance) mental health, the economy, societal damage, all have to be included into the bigger picture when considering if the West generally have followed the right actions

    Tonyb

    • Tony –

      > Both death rates in Sweden and the UK made worse by the decision to send infected or non tested patients from hospitals into care homes and not protecting the hospitals which in themselves became vectors of infection.

      I don’t knew where your getting this from. I’ll look at your link. But Sweden is noted for not even treating COVID infected older people in congregate living with oxygen, let alone transporting them to hospitals – because they were focused on sacrificing those lives for the sake of not allowing thise people to infect others.

      Also:

      • I remember posting a link last year about the similarity between Sweden the UK and I believe Italy, in not protecting their care homes properly with a resulting high mortality.

        tonyb

      • Tony –

        > I remember posting a link last year about the similarity between Sweden the UK and I believe Italy, in not protecting their care homes properly with a resulting high mortality.

        Yes. The Swedish policies for how they handled infected older people in congregate living undoubtedly contributed in some ways to their high mortality rate. On the other hand, they enacted those policies in order to limit spread – mitigating their mortality rate.

        It’s complicated. That’s just one reason why people should be circumspect about trying to use Sweden as an example for how other countries should develop and implement their own policies. There are many, many other reasons.

        Trying to extrapolate across countries, imo, is most likely about confirmation bias more so than anything else – as we don’t as of yet have sufficient ability to control for confounding variables.

        Counterfactual thinking about what would have been if things were different (in a particular country) is hard.

        But Sweden enacted their policies specifically to help limit the spread in care homes. Sonrhres a problem with your logic.

      • Tony –

        The other thing about the higher mortality in Sweden is that it reverse a fairly consistent trend of a drop in the mortality rate – so the increse this year should be viewed in that context. Just saying something like it isn’t unprecedented misses some of thst context.

      • Tony –

        Finally, excess mortality is a very noisy statistic for evaluating the impact of the pandemic. Again, there are a lot of confounding variables – for example deaths that didn’t occur because of less travel, or fewer deaths from the seasonal flu. Of course the uncertainties can run in either direction. Sure, it’s a statistic that has meaning but it’s certainly not sufficient for measuring the impact of the pandemic – of only because focusing on deaths doesn’t account for the enormous amount of serous illness and hospitalizations, and the enormous economic costs.

    • Yes Tony. CDC data shows the same thing for the US. Age adjusted mortality has been falling since 1900 and 2020 will come in at about the 2000 levels. This data is never shown because it’s not as scary as absolute fatality numbers.

      Bevard’s tweet is misleading. First he truncated the scale of the second graph. The first graph supports Tony’s assertions.

  68. Joe - the non epidemiologist

    Joshua – Bevand statement is very misleading. Observed mortality in Sweden for 2020 with covid is below every year prior to 2013 with the exception of the 1950’s .

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