Evaluating effectiveness of public health intervention strategies for mitigating COVID‐19 pandemic

Coronavirus disease 2019 (COVID‐19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non‐pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay‐at‐home order, mandatory facial mask in public in response to the rapid spread of COVID‐19. To evaluate the effectiveness of these NPIs, we propose a nested case‐control design with propensity score weighting under the quasi‐experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state‐level pre‐intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID‐19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non‐White population are at greater risk of increased [Formula: see text] associated with reopening bars.