IMO what’s needed here is detailed empirical analysis. There are many places round the world that have had spread that was only weakly controlled. If you get the % seropositive for a bunch of places, you could (to some extent) extrapolate to Europe/US/East Asia, where there’s currently more control. Here’s where I’d look:
Brazil has had a raging epidemic for quite a few months. % positive tests is currently >70%. It seems very likely that some towns have hit herd immunity. Similar story for South Africa and Mexico. (Many other countries have similarly bad epidemics, but these three have relatively good data.)
Some Indian states had >25% seropositive in studies that started in early July and there are huge number of new cases since then. Again, some towns have probability hit herd immunity.
Could also look at villages near Bergamo in Italy.
This study found 16% seropositive in a small town in Germany (e.g. with low population density). This town was locked down after an outbreak and so the 16% almost certainly underestimates the herd immunity threshold. This study was done pretty carefully (though the lead author has an axe to grind).
In SIR models you can overshoot herd immunity, right? As such, I’m not sure I should take ~30% seroprevalence as strong evidence that herd immunity is greater than ~20%. That being said, it’s hard to understand how you could have ~70% seroprevalence if herd immunity is ~20%.
To be clear, I think the 71% result needs more investigation and (on priors) is probably lower. Yes, there is reason to expect overshoot. It seems the amount of overshoot would vary based on (a) NPIs being taken at the time (e.g. are some people never leaving the house) and (b) proportion of people who have cross-immunity or innate reduced susceptibility. (In principle, you could imagine 80% of people in a town live as normal and 20% won’t leave the house till the pandemic is over.) Again, I think if we did a lot of studies, we’d get a sense of both the minimum herd immunity threshold and the variability in overshoot.
IMO what’s needed here is detailed empirical analysis. There are many places round the world that have had spread that was only weakly controlled. If you get the % seropositive for a bunch of places, you could (to some extent) extrapolate to Europe/US/East Asia, where there’s currently more control. Here’s where I’d look:
Brazil has had a raging epidemic for quite a few months. % positive tests is currently >70%. It seems very likely that some towns have hit herd immunity. Similar story for South Africa and Mexico. (Many other countries have similarly bad epidemics, but these three have relatively good data.)
Peru has had a bad epidemic. There’s a serology studying showing 71% seroprevalence in a town that was known to be very badly hit. It’s probably lower than 71% but would be good to investigate. https://twitter.com/isabelrodbar/status/1285456607065681921
Some Indian states had >25% seropositive in studies that started in early July and there are huge number of new cases since then. Again, some towns have probability hit herd immunity.
Could also look at villages near Bergamo in Italy.
This study found 16% seropositive in a small town in Germany (e.g. with low population density). This town was locked down after an outbreak and so the 16% almost certainly underestimates the herd immunity threshold. This study was done pretty carefully (though the lead author has an axe to grind).
In SIR models you can overshoot herd immunity, right? As such, I’m not sure I should take ~30% seroprevalence as strong evidence that herd immunity is greater than ~20%. That being said, it’s hard to understand how you could have ~70% seroprevalence if herd immunity is ~20%.
To be clear, I think the 71% result needs more investigation and (on priors) is probably lower. Yes, there is reason to expect overshoot. It seems the amount of overshoot would vary based on (a) NPIs being taken at the time (e.g. are some people never leaving the house) and (b) proportion of people who have cross-immunity or innate reduced susceptibility. (In principle, you could imagine 80% of people in a town live as normal and 20% won’t leave the house till the pandemic is over.) Again, I think if we did a lot of studies, we’d get a sense of both the minimum herd immunity threshold and the variability in overshoot.