Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

By Seth R. Flaxman, Swapnil Mishra, A. Gandy, H. Unwin, T. Mellan, H. Coupland, C. Whittaker, Harrison Zhu, Tresnia Berah, J. Eaton and 48 more. 2020

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Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “…is dealt with by testing models in forecasting scenarios (i.e., stress testing), and constraining the problem with domain knowledge and external data, for example about viral dynamics in patients (informing D) and contact patterns in the population (informing β) (see, e.g. Flaxman et al., 2020).”
Inferring the effectiveness of government interventions against COVID-19 (2020, background): “Finally, as in related models (1, 6), both the reported cases and deaths follow a negative binomial output distribution with separate inferred dispersion parameters for cases and deaths.”
Bayesian Workflow (2020, methodology): “An excellent example of this is the Imperial College repository for models and scripts to estimate deaths and cases for COVID-19 (Flaxman et al., 2020).”
Agile and adaptive governance in crisis response: Lessons from the COVID-19 pandemic (2020, neutral): “The lockdowns seem to have worked, as social distancing and staying at home effectively reduced transmission of the coronavirus, saving vast numbers of lives (Flaxman et al., 2020).”