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
that's a good point-- the assumption would be that cases are not clustered within the region because they reflect spread before recognition of outbreak. The adjustment made in the Santa Clara serology study, based on the/ethnicity/zip code, made the crude rate nearly double
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Indeed. I personally don't see the logic - it seems to me that it's very unlikely that there has been even community spread, but that's the main assumption when adjusting in this way. It works for things like diabetes, but for infectious disease outbreaks?
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