The limits of estimating COVID-19 intervention effects using Bayesian models

To limit the rapid spread of COVID-19, most governments have introduced different non-pharmaceutical interventions, which might have severe costs for society. Therefore, it is crucial to evaluate the most cost-effective interventions, using, for instance, Bayesian modelling. Such modelling efforts have deemed lockdown to account for 81% of the reduction in R​ ₀ , contributing to government policies. Here, we show that these conclusions are unsupported and that policies therefore should not be based on these studies.

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