COVID-19 severity determinants inferred through ecological and epidemiological modeling

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease c...

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Autores principales:	Sofija Markovic, Andjela Rodic, Igor Salom, Ognjen Milicevic, Magdalena Djordjevic, Marko Djordjevic
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	COVID-19 Disease severity Ecological regression analysis Epidemiological model Environmental factors Machine learning Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/edb7e12d02004a1298fdd6b1a9b726a4
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spelling	oai:doaj.org-article:edb7e12d02004a1298fdd6b1a9b726a42021-12-04T04:35:19ZCOVID-19 severity determinants inferred through ecological and epidemiological modeling2352-771410.1016/j.onehlt.2021.100355https://doaj.org/article/edb7e12d02004a1298fdd6b1a9b726a42021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352771421001452https://doaj.org/toc/2352-7714Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.Sofija MarkovicAndjela RodicIgor SalomOgnjen MilicevicMagdalena DjordjevicMarko DjordjevicElsevierarticleCOVID-19Disease severityEcological regression analysisEpidemiological modelEnvironmental factorsMachine learningMedicine (General)R5-920ENOne Health, Vol 13, Iss , Pp 100355- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Disease severity Ecological regression analysis Epidemiological model Environmental factors Machine learning Medicine (General) R5-920
spellingShingle	COVID-19 Disease severity Ecological regression analysis Epidemiological model Environmental factors Machine learning Medicine (General) R5-920 Sofija Markovic Andjela Rodic Igor Salom Ognjen Milicevic Magdalena Djordjevic Marko Djordjevic COVID-19 severity determinants inferred through ecological and epidemiological modeling
description	Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.
format	article
author	Sofija Markovic Andjela Rodic Igor Salom Ognjen Milicevic Magdalena Djordjevic Marko Djordjevic
author_facet	Sofija Markovic Andjela Rodic Igor Salom Ognjen Milicevic Magdalena Djordjevic Marko Djordjevic
author_sort	Sofija Markovic
title	COVID-19 severity determinants inferred through ecological and epidemiological modeling
title_short	COVID-19 severity determinants inferred through ecological and epidemiological modeling
title_full	COVID-19 severity determinants inferred through ecological and epidemiological modeling
title_fullStr	COVID-19 severity determinants inferred through ecological and epidemiological modeling
title_full_unstemmed	COVID-19 severity determinants inferred through ecological and epidemiological modeling
title_sort	covid-19 severity determinants inferred through ecological and epidemiological modeling
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/edb7e12d02004a1298fdd6b1a9b726a4
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COVID-19 severity determinants inferred through ecological and epidemiological modeling

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