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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