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...
| المؤلفون الرئيسيون: | , , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Elsevier
2021-12-01
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| سلاسل: | Preventive Medicine Reports |
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://www.sciencedirect.com/science/article/pii/S2211335521003156 |
| _version_ | 1831578160497426432 |
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| author | Eduardo J. Simoes Chester L. Schmaltz Jeannette Jackson-Thompson |
| author_facet | Eduardo J. Simoes Chester L. Schmaltz Jeannette Jackson-Thompson |
| author_sort | Eduardo J. Simoes |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T16:06:06Z |
| format | Article |
| id | doaj.art-5d589b7e85004bb0b3b51c51a36732b4 |
| institution | Directory Open Access Journal |
| issn | 2211-3355 |
| language | English |
| last_indexed | 2024-12-17T16:06:06Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Preventive Medicine Reports |
| spelling | doaj.art-5d589b7e85004bb0b3b51c51a36732b42022-12-21T21:41:56ZengElsevierPreventive Medicine Reports2211-33552021-12-0124101624Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemicEduardo J. Simoes0Chester L. Schmaltz1Jeannette Jackson-Thompson2University of Missouri School of Medicine, Department of Health Management and Informatics, CE707 CS&E Bldg., DC006.00 Columbia, MO 65212, USA; MU Institute for Data Science and Informatics, USA; Corresponding author at: University of Missouri School of Medicine, Department of Health Management and Informatics, CE707 CS&E Bldg., DC006.00 Columbia, MO 65212, USA.University of Missouri School of Medicine, Department of Health Management and Informatics, CE707 CS&E Bldg., DC006.00 Columbia, MO 65212, USA; Missouri Cancer Registry and Research Center (MCR-ARC), USAUniversity of Missouri School of Medicine, Department of Health Management and Informatics, CE707 CS&E Bldg., DC006.00 Columbia, MO 65212, USA; MU Institute for Data Science and Informatics, USA; Missouri Cancer Registry and Research Center (MCR-ARC), USABy 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.http://www.sciencedirect.com/science/article/pii/S2211335521003156COVID-19Associated factorsPredictionNB modelPoisson model |
| spellingShingle | Eduardo J. Simoes Chester L. Schmaltz Jeannette Jackson-Thompson Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic Preventive Medicine Reports COVID-19 Associated factors Prediction NB model Poisson model |
| 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 | COVID-19 Associated factors Prediction NB model Poisson model |
| url | http://www.sciencedirect.com/science/article/pii/S2211335521003156 |
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