What motivated mitigation policies? A network-based longitudinal analysis of state-level mitigation strategies

William Fries ()
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William Fries: University of Arizona

Journal of Computational Social Science, 2023, vol. 6, issue 2, No 14, 803-815

Abstract: Abstract Understanding which factors informed pandemic response can help create a more nuanced perspective on how each state of the United States handled the crisis. To this end, we create various networks linking states together based on their similarity in mitigation policies, politics, geographic proximity, and COVID-19 case data. We use these networks to analyze the correlation between pandemic policies and politics, location, and case-load from January 2020 through March 2022. We show that the best predictors of a state’s response are an aggregate political affiliation rather than solely governor affiliation as others have shown. Surprisingly, state governor’s political affiliation was the least predictive of mitigation policy strength when compared with the other measures of political affiliation. Further, we illustrate that political similarity is heavily correlated with mitigation policy similarity among states from June 2020 to June 2021, but has little impact on policy after June 2021. In contrast, geographic proximity and daily incidence are not consistently correlated with state’s having similar mitigation policies.

Keywords: Network Analysis; Politics and COVID-19 policies; Quadratic analysis procedure; COVID-19 non-pharmaceutical intervention (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42001-023-00214-x

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