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).
4/7 Heterogeneity models leading to low-initial-HIT interpretations are perfectly reconciled with data if we think like (2) but homogeneity models leading to high-initial-HIT may appear better if we think like (1).
5/7 This pandemic has been handled largely according to interpretation (1) but it remains to be established whether (2) might have been more appropriate. [Attempts to disprove either (1) or (2) have not been conclusive to this date.]
6/7 Importance of (1) vs (2) distinction is more than academic. Epidemic waves tend to be more powerful and require more mitigation with little margin for tradeoffs in scenario (1), but would be great news for handling future pandemics if reality proved more like (2).
7/7 Surely future research will clarify. I hope that enough of us remain motivated and that we get enough answers before the next pandemic.
Note: The image is a sketch to illustrate the idea. Not from a model this time.
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Don't know how common this feeling is among mathematical epidemiologists but as someone who has worked on population dynamics of infection & immunity for 20 years I felt hopeless to see herd immunity threshold (HIT) concept degenerating in front of my eyes during pandemic. Thread
1/n HIT is an abstract concept essential to our work but it was hijacked early in the Covid-19 pandemic and disseminated with all sorts of distortions that prevented the impact of its application by those qualified.
2/n A population invaded by an infectious agent (say a virus) achieves HIT when the sum of the immunities acquired by all its individuals is such that the virus cannot cause another epidemic wave.
Our newest #COVID19 paper (with Marcelo Ferreira @ChikinaLab@WesPegden@rjaaguas) is on medRxiv. Individual variation models applied to England and Scotland.
With 20% of their populations immunised by natural infection and their vulnerable vaccinated, many countries, like England and Scotland, appear to be in a comfortable position to redirect remaining vaccines to more susceptible regions.
More susceptible regions = closer to white on a combination of both these maps:
1/ I want to share a special moment with all who happen to be looking this way. This is the happiest I've been since April 27, when I posted on medRxiv a preprint with title "Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold".
2/ In that preprint we study two mathematical models of the COVID-19 pandemic: one accounting for individual variation in susceptibility to infection; the other accounting for individual variation in exposure to infection (heterogeneous connectivity).
3/ We describe how both models tell us that herd immunity thresholds are expected to be lower when individual variation is higher. Many colleagues have posted very insightful comments on our work and here I want to highlight @joel_c_miller and @BallouxFrancois.
TWIMC: I've been Mathematician and Mother for ~30 years (now also Grandmother) and this is what matters. I spent the last 10 years studying individual variation on characteristics that are under selection but that have no heritability repercussions in the time scale under study.
In these studies I have "used" (please make a note of this word) primarily host-pathogen systems but my curiosity for completely different systems was particularly vivid last year and I was happily moving away from infectious diseases when the pandemic started.
Early in the Covid pandemic I started being approached by reporters who wanted to understand what was happening and what to expect over times to come. To respond to them I started studying the data and interpreting it in light of my models.
1/ Reducing transmission is not the only way of preventing deaths. From health records we can predict who is at risk of severe or fatal disease (medrxiv.org/content/10.110…, PLoS Medicine in press) and offer them shielding.
2/ Shielding was less than optimal in the first wave, because (at least in Scotland) it was implemented too late. But we are better prepared now.
3/ The policy option of shielding the vulnerable for a short time while allowing the epidemic to run to completion among the young and fit should not be ruled out (medrxiv.org/content/10.110…).
1/ Heterogeneous susceptibility and exposure to infection are under selection by the force of infection. Highly susceptible and highly connected individuals tend to be infected earlier and removed from the susceptible pool earlier.
2/ Mean susceptibility and mean connectivity in residual susceptible pool decrease over time lowering cumulative attack rate (CAR). Models that do not account for complete variation in those characteristics are biased towards overpredicting CAR and herd immunity threshold (HIT).
3/ Correcting models by accounting for observed factors is insufficient. To ensure that variation (observable and unobservable) is captured completely we build a distribution into the models and infer its variance by fitting to epidemic curves.