10/n The authors then estimate the interventions required to bring Reff down to 1: - close schools - close universities - close some businesses - limit gatherings to 10 peoplepic.twitter.com/fNpfSmoGuB
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I think in general, the outcome classification for this study is probably the strongest I've seen. Using cases and deaths to infer Reff at a timepoint gives you a pretty good, standardized outcome that is not as impacted by variations in testing and similar
Can you explain why covid-cases and deaths as used here avoids the differential testing issue? Not doubting, curious about the reasoning. Shouldn't all cause be less prone to such biases? Shouldn't we also expect diff efficacy depending on demos affected at the time of adoption?
To me, it's mostly about consistency. Testing changes over time, but less quickly than case numbers. Incorporating deaths into this - which usually don't change substantially over time - should give a much more stable estimate of Reff imo
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