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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So far no good answer that I can see. Infectious disease epis, any thoughts?
@trentyarwood@peripatetical@aetiologyShow this threadThanks. Twitter will use this to make your timeline better. UndoUndo
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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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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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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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