Reduction of COVID-19 Incidence and Nonpharmacologic Interventions: Analysis Using a US County–Level Policy Data Set

Abstract

Background Worldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States. Objective The purpose of this study was to create a granular NPI data set 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 7 key NPIs for 1320 US counties. Results This open-source data set is the largest and most comprehensive collection of county NPI policy data 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 <.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases ( P =.004). 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). 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 data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.

Early public adherence with and support for stay-at-home COVID-19 mitigation strategies despite adverse life impact: a transnational cross-sectional survey study in the United States and Australia

“…Over the past year, Australia capitalized on the broad support for stringent mitigation measures documented herein, implementing widescale testing, contact tracing, and, in some cases, strict mitigation measures (eg, mandatory mask usage in public, physical distancing, and quarantining as necessary to contain regional outbreaks). In contrast, the United States did not capitalize on this broad initial support for stringent mitigation measures, which were effective in reducing community mobility [ 37 ] and slowing community transmission of SARS-CoV-2 [ 38 ]. Jurisdictions across the US opted instead to lift restrictions, which was associated with increased mobility [ 39 ], before testing for SARS-CoV-2 infection was readily available and widespread community transmission of COVID-19 was contained.…”

Differences in rapid increases in county-level COVID-19 incidence by implementation of statewide closures and mask mandates — United States, June 1–September 30, 2020

“…The findings in this report are subject to several limitations. First, policy data were limited to mandatory statewide mitigation measures; therefore, data did not account for county-specific variability or nonmandatory recommendations [27] . For example, some states issued orders that applied to certain counties, and others authorized counties to receive variances if certain thresholds are met (e.g., low COVID-19 test percent positivity).…”

Evolution and Epidemic Spread of SARS-CoV-2 in Colombia: A Year into the Pandemic

“…Relaxation of control events may lead to a subsequent increase in new cases, facilitating spread and potential emergence and dissemination of novel genetic variants and the presentation of new peaks of greater magnitude [ 12 , 17 , 57 ]. Likewise, it is very likely that the population’s perception of risk in relation to the magnitude of the pandemic, which has been shown to be positively correlated with compliance with the measures, will vary over time [ 19 , 21 , 22 , 37 ]. For this reason, in certain periods of time, the low compliance with the measures defined by the government has become more evident, which can be reflected in the increase in the number of cases ( Figure 1 a and Figure 4 ).…”

“…The complexity in the response of the government, added to the low compliance of the measures by the population, play a fundamental role in the dynamics of the pandemic, which is evident in the model implemented [ 12 , 16 , 18 , 37 , 58 ]. Until massive vaccination is widely fulfilled, key preventive measures, such as social distancing, use of personal protective devices, universal symptom survey and home confinement for positive cases, must continue in order to keep R t < 1, until a significant portion of the population achieves immunity.…”

“…Details on the methodology used to calculate Rt are publicly available online in (accessed on 24 February 2021) [ 36 ]. This model has been used mainly in the United States to calculate and follow the Rt dynamics in different regions, and likewise, to evaluate control measures [ 18 , 37 ]. According to what is described in the GitHub repository of the Rt.Live model, first, this model uses a simple generative logic to explain how an initial pool of infected people spreads the disease at each timepoint, according to the current reproduction factor.…”