10/n In fact, it appears that Ioannidis has continued to exclude any government reports, which is still an issue (remember, governments are doing most of the testing!)
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21/n If we instead compare the IFR of influenza calculated from seroprevalence studies and official death counts to the same for COVID-19, we see a VERY different picture
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22/n The HIGHEST IFR estimate for influenza using this methodology, based on a 2014 systematic review, is 0.01% That's 18x lower than the lowest reasonable estimate of COVID-19 IFRhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/ …
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23/n More broadly, if we look at the total range, the IFR of COVID-19 calculated from seroprevalence data appears to be around 50-100x higher than the same number for influenza
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24/n This is actually a serious flaw with the paper - the author has chosen only to pursue corrections of the data that push the IFR lower. If we were to account for excess mortality attributable to COVID-19 - based on published research - the IFRs would all jump substantially
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25/n Now, there are some excellent improvements to the paper For example, much of the language in the discussion/conclusion has been correctedpic.twitter.com/KkVboq3eDO
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26/n There are still odd, emotive phrases ("blind lockdown"), but the paper no longer describes COVID-19 as common and mild, which was clearly incorrect
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27/n However, overall this paper still suffers from many of the issues I previously raised, and seems to still substantially underestimate the IFR of COVID-19
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28/n I should be clear that I am not speculating in any way about the reasoning behind these decisions. The fact that the paper underestimates IFR is a problem, but we can't really know why these decisions were made
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End of conversation
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