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Assuming binomial sampling, one obtains a 95% CI of 0.14%-0.74%. While the authors report such a CI in the main text, they hide it for the numbers in the abstract. What is the rationale of presenting numbers with a precision that does not exist? This is somewhat concerning (6/n)
But there is more. According to this article from @hfeldwisch, they did not include all deaths that stem from infections up until the end of the study period. Instead of 7, there were probably 13 deaths. (7/n)https://medwatch.de/2020/11/26/die-ungezaehlten-todesfaelle-aus-gangelt/ …
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)
makes sense? "Bis Anfang Oktober verstarben weitere sechs Menschen aus Gangelt. Mehrere von ihnen verstarben mehr als drei Monate nach dem positiven Test.Von diesen seien nur drei Todesfälle mit hoher Wahrscheinlichkeit nicht auf die vorangegangene Coronainfektion zurückzuführen"
Was gibt es da nicht zu verstehen? Also sind mindestens drei der sechs Verstorbenen sicher noch zu den sieben dazu zu rechnen. Wer lesen kann, ist im Vortei 
Couldn't you just ask him what this all means?
There is no autopsy, no differentiation between death with and by corona. To correct for that, you have to substract the total number of all-other-cause expected death within the corona positives of ~2000, which would be around 23 per year / 17 march till october.
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