Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Sep 27, 2020
Date Accepted: Dec 8, 2020
Date Submitted to PubMed: Dec 10, 2020
Generation and analysis of U.S. county-level policy dataset demonstrates correlations of COVID-19 policies with reduced incidence
ABSTRACT
Background:
Worldwide, non-pharmacologic interventions (NPIs) have been the main tool used to mitigate the Coronavirus Disease (COVID-19) pandemic. While preliminary research across the globe has shown NPI policy to be effective, there is currently a lack of information on NPI effectiveness in the United States.
Objective:
The purpose of this study was to create a granular NPI dataset at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases.
Methods:
Using a standardized crowdsourcing methodology, we collected time series data on seven key NPIs for 1,320 U.S. counties.
Results:
This open source dataset is the largest and most comprehensive county NPI policy dataset and meets the need for higher resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P < 0.001). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R = 0.21) and elected leadership (R = 0.22). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (P = 0.0043).
Conclusions:
This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this dataset will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.
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Copyright
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