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COVID-19 deaths: Which explanatory variables matter the most?
More than a year since the appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), many questions about the disease COVID-19 have been answered; however, many more remain poorly understood. Although the situation continues to evolve, it is crucial to understand what factors may b...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022803/ https://www.ncbi.nlm.nih.gov/pubmed/35446873 http://dx.doi.org/10.1371/journal.pone.0266330 |
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author | Riley, Pete Riley, Allison Turtle, James Ben-Nun, Michal |
author_facet | Riley, Pete Riley, Allison Turtle, James Ben-Nun, Michal |
author_sort | Riley, Pete |
collection | PubMed |
description | More than a year since the appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), many questions about the disease COVID-19 have been answered; however, many more remain poorly understood. Although the situation continues to evolve, it is crucial to understand what factors may be driving transmission through different populations, both for potential future waves, as well as the implications for future pandemics. In this report, we compiled a database of more than 28 potentially explanatory variables for each of the 50 U.S. states through early May 2020. Using a combination of traditional statistical and modern machine learning approaches, we identified those variables that were the most statistically significant, and, those that were the most important. These variables were chosen to be fiduciaries of a range of possible drivers for COVID-19 deaths in the USA. We found that population-weighted population density (PWPD), some “stay at home” metrics, monthly temperature and precipitation, race/ethnicity, and chronic low-respiratory death rate, were all statistically significant. Of these, PWPD and mobility metrics dominated. This suggests that the biggest impact on COVID-19 deaths was, at least initially, a function of where you lived, and not what you did. However, clearly, increasing social distancing has the net effect of (at least temporarily) reducing the effective PWPD. Our results strongly support the idea that the loosening of “lock-down” orders should be tailored to the local PWPD. In contrast to these variables, while still statistically significant, race/ethnicity, health, and climate effects could only account for a few percent of the variability in deaths. Where associations were anticipated but were not found, we discuss how limitations in the parameters chosen may mask a contribution that might otherwise be present. |
format | Online Article Text |
id | pubmed-9022803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90228032022-04-22 COVID-19 deaths: Which explanatory variables matter the most? Riley, Pete Riley, Allison Turtle, James Ben-Nun, Michal PLoS One Research Article More than a year since the appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), many questions about the disease COVID-19 have been answered; however, many more remain poorly understood. Although the situation continues to evolve, it is crucial to understand what factors may be driving transmission through different populations, both for potential future waves, as well as the implications for future pandemics. In this report, we compiled a database of more than 28 potentially explanatory variables for each of the 50 U.S. states through early May 2020. Using a combination of traditional statistical and modern machine learning approaches, we identified those variables that were the most statistically significant, and, those that were the most important. These variables were chosen to be fiduciaries of a range of possible drivers for COVID-19 deaths in the USA. We found that population-weighted population density (PWPD), some “stay at home” metrics, monthly temperature and precipitation, race/ethnicity, and chronic low-respiratory death rate, were all statistically significant. Of these, PWPD and mobility metrics dominated. This suggests that the biggest impact on COVID-19 deaths was, at least initially, a function of where you lived, and not what you did. However, clearly, increasing social distancing has the net effect of (at least temporarily) reducing the effective PWPD. Our results strongly support the idea that the loosening of “lock-down” orders should be tailored to the local PWPD. In contrast to these variables, while still statistically significant, race/ethnicity, health, and climate effects could only account for a few percent of the variability in deaths. Where associations were anticipated but were not found, we discuss how limitations in the parameters chosen may mask a contribution that might otherwise be present. Public Library of Science 2022-04-21 /pmc/articles/PMC9022803/ /pubmed/35446873 http://dx.doi.org/10.1371/journal.pone.0266330 Text en © 2022 Riley et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Riley, Pete Riley, Allison Turtle, James Ben-Nun, Michal COVID-19 deaths: Which explanatory variables matter the most? |
title | COVID-19 deaths: Which explanatory variables matter the most? |
title_full | COVID-19 deaths: Which explanatory variables matter the most? |
title_fullStr | COVID-19 deaths: Which explanatory variables matter the most? |
title_full_unstemmed | COVID-19 deaths: Which explanatory variables matter the most? |
title_short | COVID-19 deaths: Which explanatory variables matter the most? |
title_sort | covid-19 deaths: which explanatory variables matter the most? |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022803/ https://www.ncbi.nlm.nih.gov/pubmed/35446873 http://dx.doi.org/10.1371/journal.pone.0266330 |
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