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Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic
By 21 October 2020, the coronavirus disease (COVID-19) epidemic in the United States (US) had infected 8.3 million people, resulting in 61,364 laboratory-confirmed hospitalizations and 222,157 deaths. Currently, policymakers are trying to better understand this epidemic, especially the human-to-huma...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545716/ https://www.ncbi.nlm.nih.gov/pubmed/34722135 http://dx.doi.org/10.1016/j.pmedr.2021.101624 |
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author | Simoes, Eduardo J. Schmaltz, Chester L. Jackson-Thompson, Jeannette |
author_facet | Simoes, Eduardo J. Schmaltz, Chester L. Jackson-Thompson, Jeannette |
author_sort | Simoes, Eduardo J. |
collection | PubMed |
description | By 21 October 2020, the coronavirus disease (COVID-19) epidemic in the United States (US) had infected 8.3 million people, resulting in 61,364 laboratory-confirmed hospitalizations and 222,157 deaths. Currently, policymakers are trying to better understand this epidemic, especially the human-to-human transmissibility of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in relation to social, populational, air travel related and environmental exposure factors. Our study used 50 US states’ public health surveillance datasets (January 1-April 1, 2020) to measure associations of confirmed COVID-19 cases, hospitalizations and deaths with these variables. Using the resulting associations and multivariate regression (Negative Binomial and Poisson), predicted cases, hospitalizations and deaths were generated for each US state early in the epidemic. Factors associated with a significantly increased risk of COVID-19 disease, hospitalization and death included: population density, enplanement, Black race and increased sun exposure; in addition, COVID-19 disease and hospitalization were also associated with morning humidity. Although predictions of the number of cases, hospitalizations and deaths due to COVID-19 were not accurate for every state, those states with a combination of large number of enplanements, high population density, high proportion of Black residents, high humidity or low sun exposure may expect more rapid than expected growth in the number of COVID-19 events early in the epidemic. |
format | Online Article Text |
id | pubmed-8545716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-85457162021-10-26 Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic Simoes, Eduardo J. Schmaltz, Chester L. Jackson-Thompson, Jeannette Prev Med Rep Regular Article By 21 October 2020, the coronavirus disease (COVID-19) epidemic in the United States (US) had infected 8.3 million people, resulting in 61,364 laboratory-confirmed hospitalizations and 222,157 deaths. Currently, policymakers are trying to better understand this epidemic, especially the human-to-human transmissibility of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in relation to social, populational, air travel related and environmental exposure factors. Our study used 50 US states’ public health surveillance datasets (January 1-April 1, 2020) to measure associations of confirmed COVID-19 cases, hospitalizations and deaths with these variables. Using the resulting associations and multivariate regression (Negative Binomial and Poisson), predicted cases, hospitalizations and deaths were generated for each US state early in the epidemic. Factors associated with a significantly increased risk of COVID-19 disease, hospitalization and death included: population density, enplanement, Black race and increased sun exposure; in addition, COVID-19 disease and hospitalization were also associated with morning humidity. Although predictions of the number of cases, hospitalizations and deaths due to COVID-19 were not accurate for every state, those states with a combination of large number of enplanements, high population density, high proportion of Black residents, high humidity or low sun exposure may expect more rapid than expected growth in the number of COVID-19 events early in the epidemic. 2021-10-25 /pmc/articles/PMC8545716/ /pubmed/34722135 http://dx.doi.org/10.1016/j.pmedr.2021.101624 Text en © 2021 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Simoes, Eduardo J. Schmaltz, Chester L. Jackson-Thompson, Jeannette Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic |
title | Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic |
title_full | Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic |
title_fullStr | Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic |
title_full_unstemmed | Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic |
title_short | Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic |
title_sort | predicting coronavirus disease (covid-19) outcomes in the united states early in the epidemic |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545716/ https://www.ncbi.nlm.nih.gov/pubmed/34722135 http://dx.doi.org/10.1016/j.pmedr.2021.101624 |
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