Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding f
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(2020) 20:298
RESEARCH ARTICLE
Open Access
Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations Jeff Lane* , Michelle M. Garrison, James Kelley, Priya Sarma and Aaron Katz
Abstract Background: In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states “re-open.” Methods: We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0–5 for each domain. Results: We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding. Conclusion: This detailed intensity framework reveals the granularity and nuance between social distancing policy responses. Developing standardized approaches for constructing policy taxonomies and coding processes may facilitate more rigorous policy analysis and improve disease modeling efforts.
Background The first confirmed case of COVID-19 occurred in the United States (U.S) in Washington State on January 20, 2020 [1]. Non-pharmaceutical interventions, such as quarantines and mass social distancing, were the primary public health strategy for blunting COVID-19 spread. As confirmed case counts climbed, state, county, and municipal governments adopted policies recommending or requiring actions to reduce social density and slow the * Correspondence: [email protected] University of Washington School of Public Health, Seattle, WA, USA
progression of the outbreak. The timing and intensity of social distancing policy responses has varied. Multiple efforts sought to rapidly code these social distancing policy responses for analysis [2–11]. Social distancing policy coding has been critical to COVID-19 disease models that have influenced policy decision-making whether to impose or ease social distancing approaches. For example, Dr. Deborah Birx, the U.S. Coronavirus Response Coordinator, has repeatedly cited the COVID-19 projections prepared by the University of Washington’s Institute for Health Metrics and Evaluation (IHME) [12].
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any m
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