Evaluation of Effectiveness of Mitigation Strategies for COVID-19 Pandemic
Video
Team Information
Team Members
Shanghong Xie, Postdoctoral Researcher, Biostatistics, Columbia University Mailman School of Public Health
Qinxia Wang, PhD Candidate, Biostatistics, Columbia University Mailman School of Public Health
Faculty Advisor: Yuanjia Wang, Professor, Biostatistics, Columbia University Mailman School of Public Health
Abstract
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.
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Contact: Shanghong Xie (use form to send email)
