1/ Many times I have been asked why communication around herd immunity threshold (HIT) was so confusing in this pandemic. I have even been asked whether experts really understand it. Here is my answer:
2/ The concept is well understood among mathematical epidemiologists. In my view what went terribly wrong was the politicised way in which the HIT was used in this pandemic.
3/ The HIT is the percentage of the population that needs to be immune (prior to vaccination immunity was a natural outcome of recovery from infection) before the epidemic peaks and subsides.
4/ The height of an epidemic is then directly related to the HIT in a way that can be derived by some relatively simple mathematics.
5/ The best utility for the HIT in a pandemic is to help generate expectations for how high the peak might be if we do nothing, so that policymakers can compare with health system capacity and decide how much to intervene. But this was not how the HIT was used.
6/ Early in the pandemic some researchers (myself included) noted that the HIT values being communicated (~70%) were probably too high because they were based on models that did not account for certain types of individual variation known to lower those thresholds.
7/ Some of the mathematical modelling community, however, resisted these ideas on the premise that given the uncertainly on model parameters it would be dangerous to admit anything other than worst-case scenarios.
8/ At this point two diverging positions formed: (1) one favouring worst-cases scenarios to promote overcaution towards covid;
9/ (2) another defending that modelling should be as unbiased as possible so that decision makers could have the most objective tools at their disposal for assessing the costs and benefits of interventions and develop balanced strategies.
10/ I think this divergence was the reason behind a most unhelpful use of the HIT in this pandemic. Position (1) became overwhelmingly dominant and many on that side tried to discredit position (2) by reporting settings where seroprevalence had exceeded lower HIT estimates.
11/ Of course this is flawed in several important ways. One is that the size of an epidemic is always bigger than the number of cases that occur until the peak (there are all those extra cases as the curve makes its way down, known as overshoot).
12/ Another is that as enough time elapses other processes occurs (such as waning immunity, viral evolution, etc) enabling subsequent epidemic waves even if HIT has been previously achieved.
13/ The expectation is that everybody will eventually be infected (and not only once) and using HIT over long stretches of time is not helpful.
14/ Unfortunately, HIT was turned into a weapon when it should have been used as a valuable tool for assessing trade-offs in the development of balanced pandemic control strategies. I hope we learn from this and do better next time.
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Then decided to write thread about what's killing me. Not depressing (on contrary). It's my duty to make the world understand this +ve thing before I die [not that I think I'll die soon; that was just in dream]
PLEASE READ IF YOU CAN!
For more than 10 years I've been researching with collaborators (including @mlipsitch@GrahamMedley) why epidemic models tend to exaggerate epidemic sizes and overestimate intervention impacts (particularly vaccines but also NPIs): journals.plos.org/plospathogens/…
Last year, prominent modelling groups dismissed our Covid work (known for incorporating individual variation in susceptibility and exposure, and estimating low herd immunity thresholds) by claiming our stylised contact-reduction profile (Rc) wasn't close to government NPIs..
England and Scotland (with our latest stylised Rc):
This week, I had a few moments to spare and implemented the same model and fittings with the stringency index that tracks government response. The results are almost identical...
No inicio da pandemia convidaram-me para integrar um daqueles grupos que fazem modelos Covid para o governo Portugues. Eu disse que nao porque queria testar um novo conceito de modelos e queria estar a vontade para fazer ciencia pura e comunicar a vontade.
Comecei a ser contatada por jornalistas que me faziam perguntas as quais eu respondia avisando sempre que os meus modelos eram diferentes dos clássicos.
Talvez nao seja surpreendente que os modelos demorem a ser aceites na especialidade embora eu tivesse alguma esperança que neste caso fosse mais rápido. Mas enfim, abordagens novas demoram a ser processadas.
As we approach endemicity new variants are expected to outcompete others faster. Reason being recovered subpopulation (less immune to novel variants as long as there is some immune escape) grows as we approach endemicity increasing the benefit of novelty.
Illustration from model:
When replacement is by immune escape the second variant (B) outcompetes previous faster than first variant (A) had done:
Tweet que e cada vez menos novidade mas infelizmente ainda nao totalmente esclarecido principalmente em Portugal:
Levando em conta heterogeneidades realistas na susceptibilidade/exposição ao SARS-CoV-2 o limiar da imunidade de grupo para linhagens iniciais tera sido menos de 30%.
Se nao tivessem emergido variantes mais transmissíveis nem vacinas (como se equacionava ha mais de 1 ano) estes limiares teriam sido ultrapassados no inverno passado. Vale o que vale mas cada vez temos mais argumentos a suportar esta afirmação.
Desde então temos vivido num equilíbrio precário entre aumento de transmissibilidade do virus (devido as variantes) e diminuição de transmissibilidade (devido as vacinas) que mantem o R efectivo em torno de 1.
1/7 Has the herd immunity threshold (HIT) been used sensibly in the Covid19 pandemic? No! Why does that matter?
2/7 An epidemic (with several waves) may last longer than expected because: (1) it had high potential to begin with and mitigation prevented it from growing vertically so it grew horizontally (single HIT number thinking); or...
3/7 (2) it didn't have as high potential as one might think but viral evolution, seasonality, waning immunity, population renewal, kept it going (dynamic HIT thinking).