Thought I'd take a leaf out of @ProfMattFox's book and use some 2x2 tables to illustrate thishttps://twitter.com/GidMK/status/1278468849294168064 …
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What this means is that if you run the test in a population where very few people who've had the disease, MOST of your positive tests will be false positives This means that your prevalence estimate might be double the true one (or more)
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If instead you run the test in a population where many people have had COVID-19, you'll underestimate the prevalence by at least 10% Both of these aren't great scenarios
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