{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T17:25:19Z","timestamp":1772472319080,"version":"3.50.1"},"reference-count":51,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["2031536"],"award-info":[{"award-number":["2031536"]}]},{"name":"National Science Foundation","award":["2031536"],"award-info":[{"award-number":["2031536"]}]},{"name":"National Science Foundation","award":["2031536"],"award-info":[{"award-number":["2031536"]}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2031536"],"award-info":[{"award-number":["2031536"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>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 \u201cstay at home\u201d 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 \u201clock-down\u201d 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0266330","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T13:30:51Z","timestamp":1650547851000},"page":"e0266330","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["COVID-19 deaths: Which explanatory variables matter the most?"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1859-456X","authenticated-orcid":true,"given":"Pete","family":"Riley","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Allison","family":"Riley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0735-7769","authenticated-orcid":true,"given":"James","family":"Turtle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michal","family":"Ben-Nun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"issue":"10223","key":"pone.0266330.ref001","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"C Huang","year":"2020","journal-title":"The lancet"},{"issue":"5","key":"pone.0266330.ref002","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"E Dong","year":"2020","journal-title":"The Lancet infectious diseases"},{"key":"pone.0266330.ref003","article-title":"COVID-19: Transmission, prevention, and potential therapeutic opportunities","author":"M Lotfi","year":"2020","journal-title":"Clinica chimica acta"},{"key":"pone.0266330.ref004","doi-asserted-by":"crossref","first-page":"109846","DOI":"10.1016\/j.chaos.2020.109846","article-title":"Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan","volume":"135","author":"F Nda\u00efrou","year":"2020","journal-title":"Chaos, Solitons & Fractals"},{"key":"pone.0266330.ref005","doi-asserted-by":"crossref","first-page":"138513","DOI":"10.1016\/j.scitotenv.2020.138513","article-title":"Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China","volume":"726","author":"J Liu","year":"2020","journal-title":"Science of the total environment"},{"issue":"19","key":"pone.0266330.ref006","doi-asserted-by":"crossref","first-page":"E497","DOI":"10.1503\/cmaj.200476","article-title":"Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada","volume":"192","author":"AR Tuite","year":"2020","journal-title":"Cmaj"},{"issue":"1","key":"pone.0266330.ref007","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1111\/ecin.12944","article-title":"When do shelter-in-place orders fight COVID-19 best? 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