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New study on #SARSCoV2 mortality in six European regions out in @PLOSMedicine. Very honored to be part of this exciting group of researchers (@AnthonyHauser5, @MichelDVM, C. Margossian, @konstantinoudis, @nicolamlow) led by our Bayesian master @JulRiouhttps://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003189 …
We used a Bayesian modeling framework to estimate the infection fatality ratio (IFR) between 0.5% and 1.4% in Austria, Baden-Württemberg, Bavaria, Lombardy, Spain and Switzerland. These estimates are in strong agreement with recent seroprevalence studies in some of these regions.pic.twitter.com/CY4IuRPDSy
Thanks to @snsf_ch and @EpiPose for financial support. @mcmc_stan code and data are available from the following @github repository:https://github.com/jriou/covid_adjusted_cfr …
How is it that the IFR (2.9%, which seems way higher than anything I've seen) was estimated to be higher than the CFR (2.4%) in the Hubei baseline?
What defines the ‘I’ in IFR? If it’s seropositivity might this underestimate number exposed who fight it off without creating detectable AB titres. Is IFR relevant to societal burden without consideration of who actually can become infected?
Cases in Switzerland with around 300k estimated are overestimated by a factor of two after my opinion and my analyses. Seroprevalence studies beside of some of it do the same overestimation.
"The predicted half-life of N-antibody was 52 days... Widely used serological tests that depend on the N-antibody will therefore significantly underestimate the prevalence of infection following the majority of infections." https://www.medrxiv.org/content/10.1101/2020.07.16.20155663v2.full.pdf …
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