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The unintended consequences of inconsistent pandemic control policies

Controlling the spread of COVID-19 - even after a licensed vaccine is available - requires the effective use of non-pharmaceutical interventions, e.g., physical distancing, limits on group sizes, mask wearing, etc(1–7). To date, such interventions have not been uniformly and/or systematically implem...

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Autores principales: Althouse, Benjamin M., Wallace, Brendan, Case, Brendan, Scarpino, Samuel V., Allard, Antoine, Berdahl, Andrew M., White, Easton R., Hebert-Dufresné, Laurent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457624/
https://www.ncbi.nlm.nih.gov/pubmed/32869043
http://dx.doi.org/10.1101/2020.08.21.20179473
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author Althouse, Benjamin M.
Wallace, Brendan
Case, Brendan
Scarpino, Samuel V.
Allard, Antoine
Berdahl, Andrew M.
White, Easton R.
Hebert-Dufresné, Laurent
author_facet Althouse, Benjamin M.
Wallace, Brendan
Case, Brendan
Scarpino, Samuel V.
Allard, Antoine
Berdahl, Andrew M.
White, Easton R.
Hebert-Dufresné, Laurent
author_sort Althouse, Benjamin M.
collection PubMed
description Controlling the spread of COVID-19 - even after a licensed vaccine is available - requires the effective use of non-pharmaceutical interventions, e.g., physical distancing, limits on group sizes, mask wearing, etc(1–7). To date, such interventions have not been uniformly and/or systematically implemented across the United States of America (US)(8). For example, even when under strict stay-at-home orders, numerous jurisdictions in the US granted exceptions and/or were in close proximity to locations with entirely different regulations in place. Here, we investigate the impact of such geographic inconsistencies in epidemic control policies by coupling high-resolution mobility, search, and COVID case data to a mathematical model of SARS-CoV-2 transmission. Our results show that while stay-at-home orders decrease contacts in most areas of the US, some specific activities and venues often see an increase in attendance. As an example, over the month of March 2020, between 10 and 30% of churches in the US saw increases in attendance; even as the total number of visits to churches declined nationally. This heterogeneity, where certain venues see substantial increases in attendance while others close, suggests that closure can cause individuals to find an open venue, even if that requires longer-distance travel. And, indeed, the average distance travelled to churches in the US rose by 13% over the same period, and over the summer, churches with more than 50 average weekly visitors saw an increase of 81% in distance visitors had to travel to attend. Strikingly, our mathematical model reveals that, across a broad range of model parameters, partial measures can often be worse than no measures at all. In the most severe cases, individuals not complying with policies by traveling to neighboring jurisdictions can create epidemics when the outbreak would otherwise have been contained. Indeed, using county-level COVID-19 data, we show that mobility from high-incidence to low-incidence associated with travel for venues like churches, parks, and gyms consistently precedes rising case numbers in the low-incidence counties. Taken together, our data analysis of nearly 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7,662 grocery stores, 13.5 million gym visitors across 5,483 gyms, 7.7 million cases across 3,195 counties, and modeling results highlight the potential unintended consequences of inconsistent epidemic control policies and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic, and the urgent need for centralized implementation and enforcement of non-pharmaceutical interventions.
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spelling pubmed-74576242020-09-01 The unintended consequences of inconsistent pandemic control policies Althouse, Benjamin M. Wallace, Brendan Case, Brendan Scarpino, Samuel V. Allard, Antoine Berdahl, Andrew M. White, Easton R. Hebert-Dufresné, Laurent medRxiv Article Controlling the spread of COVID-19 - even after a licensed vaccine is available - requires the effective use of non-pharmaceutical interventions, e.g., physical distancing, limits on group sizes, mask wearing, etc(1–7). To date, such interventions have not been uniformly and/or systematically implemented across the United States of America (US)(8). For example, even when under strict stay-at-home orders, numerous jurisdictions in the US granted exceptions and/or were in close proximity to locations with entirely different regulations in place. Here, we investigate the impact of such geographic inconsistencies in epidemic control policies by coupling high-resolution mobility, search, and COVID case data to a mathematical model of SARS-CoV-2 transmission. Our results show that while stay-at-home orders decrease contacts in most areas of the US, some specific activities and venues often see an increase in attendance. As an example, over the month of March 2020, between 10 and 30% of churches in the US saw increases in attendance; even as the total number of visits to churches declined nationally. This heterogeneity, where certain venues see substantial increases in attendance while others close, suggests that closure can cause individuals to find an open venue, even if that requires longer-distance travel. And, indeed, the average distance travelled to churches in the US rose by 13% over the same period, and over the summer, churches with more than 50 average weekly visitors saw an increase of 81% in distance visitors had to travel to attend. Strikingly, our mathematical model reveals that, across a broad range of model parameters, partial measures can often be worse than no measures at all. In the most severe cases, individuals not complying with policies by traveling to neighboring jurisdictions can create epidemics when the outbreak would otherwise have been contained. Indeed, using county-level COVID-19 data, we show that mobility from high-incidence to low-incidence associated with travel for venues like churches, parks, and gyms consistently precedes rising case numbers in the low-incidence counties. Taken together, our data analysis of nearly 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7,662 grocery stores, 13.5 million gym visitors across 5,483 gyms, 7.7 million cases across 3,195 counties, and modeling results highlight the potential unintended consequences of inconsistent epidemic control policies and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic, and the urgent need for centralized implementation and enforcement of non-pharmaceutical interventions. Cold Spring Harbor Laboratory 2020-10-28 /pmc/articles/PMC7457624/ /pubmed/32869043 http://dx.doi.org/10.1101/2020.08.21.20179473 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Althouse, Benjamin M.
Wallace, Brendan
Case, Brendan
Scarpino, Samuel V.
Allard, Antoine
Berdahl, Andrew M.
White, Easton R.
Hebert-Dufresné, Laurent
The unintended consequences of inconsistent pandemic control policies
title The unintended consequences of inconsistent pandemic control policies
title_full The unintended consequences of inconsistent pandemic control policies
title_fullStr The unintended consequences of inconsistent pandemic control policies
title_full_unstemmed The unintended consequences of inconsistent pandemic control policies
title_short The unintended consequences of inconsistent pandemic control policies
title_sort unintended consequences of inconsistent pandemic control policies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457624/
https://www.ncbi.nlm.nih.gov/pubmed/32869043
http://dx.doi.org/10.1101/2020.08.21.20179473
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