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.
3/n
Way to visualise an approximation to this is by imagining a pandemic occurring without mitigation, and people infected each day being recorded. Typically HIT is the percentage of the population who has been infected (and acquired immunity or died) by the time epidemic peaks.
4/n
This representation gave rise to numerous difficulties which I try to summarise:

A) Model-based inference. In pandemic people modify behaviour, making direct measurement of HIT impossible. Even if behaviour had not changed, HIT could only be measured after peak, which would
5/n
be too late to be useful. Mathematical epidemiologists develop models that enable indirect inference of HIT from incomplete timeseries of cases, hospitalisations and deaths. These inferences come with uncertainties difficult to quantify.
6/n
B) Differences between individuals. The representation of HIT as a percentage of the population could only be unambiguous if all immunised individuals contributed same amount to population immunity. But in reality some individuals are more susceptibly than others
7/n
and consequently their immunisation has more weight. Since the epidemic tends to affected more susceptible individuals first, HIT is attained with a lower percentage of the population having been infected. At beginning of pandemic we don't know how variable susceptibility is.
8/n
Worse, we don't know how to measure individual susceptibility directly much less to build the distribution of susceptibilities of individuals that constitute population. To circumvent this, we developed a method to infer these distributions indirectly for use in HIT research.
9/n
C) Viral evolution. Viruses undergo mutations and natural selection tends to favour those variants which are more transmissible or less naturalisable by acquired immunity. In general such variants expand in abundance and become dominant causing HIT to increase over time.
10/n
D) Seasonality. Respiratory viruses are more transmissible in cold and dry environments, which in temperate climates corresponds to winter season. Consequently timeseries that inform HIT inferences should ideally include at least one winter. So what should we do if the
11/n
pandemic begins in the spring? Wait until following winter to estimate HIT? Of course not! We begin estimation as soon as possible and update as pandemic unfolds. But we cannot stay fixed to first estimates especially given the expectation for HIT to increase in winter.
12/n
E) Population turnover. It is common for pandemic respiratory viruses to become endemic. Virus continues to circulate causing seasonal epidemics. So what happens with herd immunity? Is HIT unreachable? No, HIT is reached any time there is a seasonal epidemic.
13/n
But if HIT is reached why are there any subsequent epidemics at all? Because the population is in constant renewal with elderly ending life and susceptible babies beginning (waning immunity has similar effect as far as renewal of immunity goes). This way population immunity
14/n
declines and eventually drops below HIT again, ie, conditions to sustain epidemics are restored. Fortunately, severity tends to be much lower in comparison with initial pandemic waves. In endemic state people are naturally exposed early in life, when infection is mild, and
15/n
continue to be repeatedly exposed throughout life maintaining protective immunological memory. This process tents to lose efficacy in older ages at which point regular vaccination gains importance, such as in the case of influenza.
16/n
Finally, what I want to communicate here is that the use of HIT during the pandemic, by policy makers and media, has not been consistent with the population phenomena above. Understanding HIT is useful early in the pandemic and subsequent use requires constant update.
17/n
But the utility of the HIT becomes lower as the pandemic progresses. I would say that the utility of HIT for Covid-19 in the most affected and/or vaccinated regions expired during the first semester of 2021.
18/n
In European countries, in particular, there seems to be little sense in continuing to aim for herd immunity to the new coronavirus. The situation we are at may be identical to where we will be in 1 year, 2, 3, 4, and so on, and we cannot continue to prioritise a disease
19/n
that seems no longer more important than many others, including those caused by other respiratory viruses which have been suppressed by anti-coronavirus measures but will most likely return this coming winter.
20/n
HIT is fundamental in cost-benefit analysis of possible measures to mitigate or suppress pandemic but its estimation must be early and accurate. Biases that exaggerate HIT result in exaggerated strategies while biases that diminish HIT results in insufficient measures.
21/n
In this pandemic a complexity of factors has created a powerful tendency for HIT overestimation. Which factors and how they interacted to produce such dominant pro-lockdown current seems an important topic for transdisciplinary research in years to come.
better phrasing: "we cannot forever prioritise" instead of "we cannot continue to prioritise"

it should be clear from the thread that this is what was meant but better be sure

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More from @mgmgomes1

6 Sep
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).
Read 8 tweets
27 May
Our newest #COVID19 paper (with Marcelo Ferreira @ChikinaLab @WesPegden @rjaaguas) is on medRxiv. Individual variation models applied to England and Scotland.

Frailty variation models for susceptibility and exposure to SARS-CoV-2 medrxiv.org/content/10.110…
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: ImageImage
Read 8 tweets
24 Dec 20
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.
Read 6 tweets
9 Dec 20
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.
Read 11 tweets
4 Oct 20
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…).
Read 4 tweets
4 Oct 20
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.
Read 4 tweets

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