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This would result in an IFR of 13/1956 = 0.66% (95% CI: 0.35%-1.13%). That's interesting, because this estimate is in good agreement with the results from our statistical modeling study and the seroprevalence study from Munich. (8/n)
While that's an interesting turn of events, I still would not put too much trust into an IFR from such a small community and with such a small number of deaths. Especially when there are much bigger studies around that are based on hundreds or thousands of deaths. (9/n)
More specifically for Germany:https://twitter.com/falsel_net/status/1317069745770954759 …
https://arxiv.org/pdf/2010.05850.pdf#cite.Russell2020.07.07.20148460 … pic.twitter.com/vPMPjsLERZAs far as I remember the Gangelt study discussions, the study was always known to be clearly non-representative for germanys population. It contains a natural selection bias, as the driving cluster was carnival festivities that are mainly attended by young to medium aged people.
It's insane to still promote the initial conclusion of this study. It's interesting but local biased data. Could do the same in a similar sized village with a huge retirement home and would probably see a boosted (local) IFR. Again nice data but not valid for global conclusions.
Genau so ist es. Danke
As you wrote, with "only" you can not estimate the IFR accurately. And this is true even more so, as we know, that the probability to decrease when infected with Covid heavily depends on age. It would be far more serious, to present an IFR-function which depends on age, sex, ...
2/2 This in turn requires more data and probably one has to combine studies. I just want to say that estimating CIs with a bin. distribution is not helpful, if one knows, that the p depends so strongly on other risk-factors. Taking this into account, it will further widen the CI.
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