How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
This work addresses the reliability of NPI effectiveness estimates for policymakers and researchers, though it is incremental as it builds on existing models.
The study investigated how assumptions in models affect the estimated effectiveness of nonpharmaceutical interventions (NPIs) against COVID-19, finding that models accounting for transmission noise performed better and that published results were robust across different parameters and data.
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.