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 R t associated with reopening bars.
【저자키워드】 COVID-19, Non-pharmaceutical interventions, infectious disease modeling, Difference-in-difference, Heterogeneity of treatment effect (HTE), Quasi-experiments, 【초록키워드】 Coronavirus disease 2019, pandemic, lockdown, risk, Intervention, Transmission, Characteristics, Effectiveness, NPIs, Case-control, moderate, disease transmission, Public health intervention, Government, COVID-19 incidence, Factor, Precision, global public health, average, intervention effect, physical distance, Weighting, Effect, state, spread of COVID-19, greater, develop, evaluate, significantly, the United State, provide, reducing, assist, facial, mandatory, 【제목키워드】 Strategy, Public, Evaluating,