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

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  • Xie, Shanghong
    • Other Affiliation: Columbia University
  • Wang, Wenbo
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Wang, Qinxia
    • Other Affiliation: Columbia University
  • Wang, Yuanjia
    • Other Affiliation: Columbia University
  • Zeng, Donglin
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
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. States with a higher percentage of non-White population are at greater risk of increased R t $$ {R}_t $$ associated with reopening bars.
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  • Article
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  • In Copyright
Journal title
  • Statistics in Medicine
Language
  • English
ISSN
  • 1097-0258

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