How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

To what extent are effectiveness estimates of nonpharmaceutical interventions (NPIs) against COVID-19 influenced by the assumptions our models make? To answer this question, we investigate 2 state-of-the-art NPI effectiveness models and propose 6 variants that make different structural assumptions. In particular, we investigate how well NPI effectiveness estimates generalise to unseen countries, and their sensitivity to unobserved factors. Models that account for noise in disease transmission compare favourably. We further evaluate how robust estimates are to different choices of epidemiological parameters and data. Focusing on models that assume transmission noise, we find that previously published results are remarkably robust across these variables. Finally, we mathematically ground the interpretation of NPI effectiveness estimates when certain common assumptions do not hold. Data-driven NPI modelling is one of the best approaches for inferring NPI effect sizes. These models assume that the implementation of an NPI affects the course of a country's epidemic in a particular way. Then, using publicly available incidence and fatality data, as well as a list of NPIs with their implementation dates, the NPI model can be inverted, yielding NPI effectiveness estimates.