Apologies if you’ve already thought of this, but some quick points:
I think it’s probably wrong to assume that covid19 IFR is a static quantity.
It seems very plausible to me that (esp in the US) empirical covid-19 IFR dropped a lot over time, through a combination of better treatment and self-selection in who gets infected.
In addition, IFR varies a lot from location to location due to demographic differences.
Finally, one issue with using anti-body testing as ground truth for “once infected” that it’s plausible that people lose antibodies over time.
I agree with all of these points. In fact, with respect to (4) it is even plausible that some “once infected” people never go on to develop the kind of antibodies that are being tested for. Point (3) is why I control for age/sex, but of course there are a number of further complexities.
These further complexities, along with (1) and (2) are currently “un-modelable complexities” for me. There are just so many selection effects in play that it isn’t clear if you gain anything from trying to take them into account. Given that there are a number of papers that try to calculate some kind of IFR making a number of basic mistakes, I wanted to set out to see what the data + simple models gets if you do the maths correctly, as opposed to doing ridiculous things like caring about the median study like the Ioannadis meta-analysis does. After all, it seems like the IFR is what everyone cares about, so it would be nice if we were doing things “less wrong” here.
Apologies if you’ve already thought of this, but some quick points:
I think it’s probably wrong to assume that covid19 IFR is a static quantity.
It seems very plausible to me that (esp in the US) empirical covid-19 IFR dropped a lot over time, through a combination of better treatment and self-selection in who gets infected.
In addition, IFR varies a lot from location to location due to demographic differences.
Finally, one issue with using anti-body testing as ground truth for “once infected” that it’s plausible that people lose antibodies over time.
Thanks for the comments!
I agree with all of these points. In fact, with respect to (4) it is even plausible that some “once infected” people never go on to develop the kind of antibodies that are being tested for. Point (3) is why I control for age/sex, but of course there are a number of further complexities.
These further complexities, along with (1) and (2) are currently “un-modelable complexities” for me. There are just so many selection effects in play that it isn’t clear if you gain anything from trying to take them into account. Given that there are a number of papers that try to calculate some kind of IFR making a number of basic mistakes, I wanted to set out to see what the data + simple models gets if you do the maths correctly, as opposed to doing ridiculous things like caring about the median study like the Ioannadis meta-analysis does. After all, it seems like the IFR is what everyone cares about, so it would be nice if we were doing things “less wrong” here.