Latkes
2019-nCov crude infection fatality ratio (IFR) 0.07% and time-delay adjusted IFR 0.23%, which is several orders of magnitude smaller than the crude CFR at 4.06%
Just an early assessment.
If this is correct, then comparisons to flu make a lot of sense.
could be please interpret the data into statistics for dummies format.
5 Replies
They estimate the actual number of infections is much higher than confirmed cases from official stats - 983,006 in Wuhan City alone by mid February.
The actual fatality rate is much lower than the current official CFR - at 0.07% or 0.23% time-delay adjusted. (compared to the 2% or 3% or 4% mentioned in various media sources)
It's much less deadly than many think.
Thanks makes sense. Death toll however may be in reality higher as reported by the Chinese
The death toll may be higher but likely much closer to the reported numbers than the infection rate.
It seems what they are saying is if the denominator is much much bigger than we think, then Deaths_in_Numerator / Total_infected is much smaller than what we currently believe, not 2.5% but much smaller. The problem with this logic is that cases outside China seem to be contradicting this narrative, that is, given a very accurate Total_infected, like in South Korea, we have, as of today 2/27/20: 13 deaths in S. Korea / 2022 total infected, and so already we're at 0.64%, much higher than the 0.07% quoted in this study. And more people will die in South Korea no doubt, as time progresses. File this pre-print study under "don't panic the sheeple" IMO.
It's an educated statistical estimate based on decades of epidemiological experience. If the commonly published numbers were accurate, this would be the most unique and perfect virus in history - spreading easily at a high speed and perfectly tracked at the same time.
No way can this be inferred. This is a real pie in the sky analysis. Much more accurate data can be calculated in a month or two from the japanese cruise ship
Wait, what?
"Conclusions: We have estimated key epidemiological parameters of the transmissibility and virulence of 2019–nCov in Wuhan, China, 2019-2020 using an ecological modelling approach. The power of our approach lies in the ability to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance system."
