Hey #epitwitter, can anyone explain something to me?
I honestly don't understand why you would normalize to the population from a sample of COVID-19 serological tests. The reason we extrapolate in this way is to gain an idea of the population prevalence...
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(cont)...but in this case, we KNOW that COVID-19 is likely to be clustered. Unless your normalization for the population assumes a very uneven spread by design, presumably all you're ever going to do is overestimate the proportion of people who've been infected?
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Replying to @GidMK
I think it would only ever attempt to estimate disease history in specific populations, so ignoring the vulnerable groups and using subgroups to make the estimates cohort specific. Though the bigger problem here is finding a robust antibody test!
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Replying to @naomhgallagher
That makes sense to me with a mature epidemic, with months of disease spread, but I just can't see the logic if your confirmed case number is less than 0.1% of the population. Seems an easy way to wildly bias your results to me
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Replying to @GidMK
Yea I agree, but I think in the absence of mass population testing it would be the only option. And the current confirmed case % is so heavily influenced by testing, there’ll be many hoping it is actually a lot higher than that!
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I take your point, but I'm not sure that using another type of very flawed estimate as the truth makes more sense in the absence of population testing. Both estimates give us information, neither is true!
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