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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | One Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352771421001452 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T20:46:46Z |
| format | Article |
| id | doaj.art-edb7e12d02004a1298fdd6b1a9b726a4 |
| institution | Directory Open Access Journal |
| issn | 2352-7714 |
| language | English |
| last_indexed | 2024-12-22T20:46:46Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | One Health |
| spelling | doaj.art-edb7e12d02004a1298fdd6b1a9b726a42022-12-21T18:13:12ZengElsevierOne Health2352-77142021-12-0113100355COVID-19 severity determinants inferred through ecological and epidemiological modelingSofija Markovic0Andjela Rodic1Igor Salom2Ognjen Milicevic3Magdalena Djordjevic4Marko Djordjevic5Quantitative Biology Group, Faculty of Biology, University of Belgrade, SerbiaQuantitative Biology Group, Faculty of Biology, University of Belgrade, SerbiaInstitute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, SerbiaDepartment for Medical Statistics and Informatics, School of Medicine, University of Belgrade, SerbiaInstitute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, SerbiaQuantitative Biology Group, Faculty of Biology, University of Belgrade, Serbia; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2352771421001452COVID-19Disease severityEcological regression analysisEpidemiological modelEnvironmental factorsMachine learning |
| spellingShingle | Sofija Markovic Andjela Rodic Igor Salom Ognjen Milicevic Magdalena Djordjevic Marko Djordjevic COVID-19 severity determinants inferred through ecological and epidemiological modeling One Health COVID-19 Disease severity Ecological regression analysis Epidemiological model Environmental factors Machine learning |
| title | 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_short | COVID-19 severity determinants inferred through ecological and epidemiological modeling |
| title_sort | covid 19 severity determinants inferred through ecological and epidemiological modeling |
| topic | COVID-19 Disease severity Ecological regression analysis Epidemiological model Environmental factors Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352771421001452 |
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