Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19

Abstract

Social distancing policies are critical but economically painful measures to flatten the curve against emergent infectious diseases. As the novel coronavirus that causes COVID-19 spread throughout the United States in early 2020, the federal government issued social distancing recommendations but left to the states the most difficult and consequential decisions restricting behavior, such as canceling events, closing schools and businesses, and issuing stay-at-home orders. We present an original dataset of state-level social distancing policy responses to the epidemic and explore how political partisanship, COVID-19 caseload, and policy diffusion explain the timing of governors' decisions to mandate social distancing. An event history analysis of five social distancing policies across all fifty states reveals the most important predictors are political: all else equal, Republican governors and governors from states with more Trump supporters were slower to adopt social distancing policies. These delays are likely to produce significant, on-going harm to public health.

Mapping the Burden of COVID-19 in the United States

“…However, obtaining accurate predictions of the epidemic peak is a central challenge in emerging outbreaks due to limited, and often unreliable, data on incidence, and rapidly deployed and changing mitigation efforts. In the case of SARS-CoV-2, county-level variability in testing standards 10,11 , Nonpharmaceutical Interventions (NPIs) 12 , and timing of initial case introductions 13 limit our ability to produce accurate epidemic predictions at the weeks and months-. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

State-level variation of initial COVID-19 dynamics in the United States: The role of local government interventions

“…Each US state varies considerably across a number of important axes: wealth, access to healthcare, number of international travelers, age distribution, population density, among other factors (Chin et al 2020). In addition, much of the response to COVID-19 has been done at the state, as opposed to federal, government level in the US (Adolph et al 2020, Gostin et al 2020. We examined three hypotheses to explain the state-level variation in COVID-19 trajectories: human demographics, wealth and education indicators, and governmental interventions.…”

“…Therefore, we also examined the correlation between doubling time and state government interventions. We used information collected by Adolph et al (2020) on whether or not a state had implemented a specific action by the first day they had 25 or more cases. We adjusted this number to 150 cases for more severe restrictions like closing all non-essential business or stay at home mandates).…”

“…We adjusted this number to 150 cases for more severe restrictions like closing all non-essential business or stay at home mandates). We looked at closing of public schools, limiting large gatherings (usually of more than 10 people), restricting business, and stay at home orders (Adolph et al 2020). Of these factors, only restricting businesses, specifically restaurants, was a significant predictor of doubling time (Fig.…”

“…Clearly, controlled and randomized experiments of COVID-19 spread are not possible (White et al 2019). Therefore, although states are not independent units, we can use state-level data to understand the progression of the outbreak across different replicates within a country (Adolph et al 2020). We show that across measures of demography, education, health, and wealth, only population density was correlated with doubling time.…”