Unraveling the dynamic importance of county-level features in trajectory of COVID-19

Abstract The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread u...

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Autores principales:	Qingchun Li, Yang Yang, Wanqiu Wang, Sanghyeon Lee, Xin Xiao, Xinyu Gao, Bora Oztekin, Chao Fan, Ali Mostafavi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ef266603da604585805112e255324a7e
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spelling	oai:doaj.org-article:ef266603da604585805112e255324a7e2021-12-02T17:44:54ZUnraveling the dynamic importance of county-level features in trajectory of COVID-1910.1038/s41598-021-92634-w2045-2322https://doaj.org/article/ef266603da604585805112e255324a7e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92634-whttps://doaj.org/toc/2045-2322Abstract The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle.Qingchun LiYang YangWanqiu WangSanghyeon LeeXin XiaoXinyu GaoBora OztekinChao FanAli MostafaviNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qingchun Li Yang Yang Wanqiu Wang Sanghyeon Lee Xin Xiao Xinyu Gao Bora Oztekin Chao Fan Ali Mostafavi Unraveling the dynamic importance of county-level features in trajectory of COVID-19
description	Abstract The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle.
format	article
author	Qingchun Li Yang Yang Wanqiu Wang Sanghyeon Lee Xin Xiao Xinyu Gao Bora Oztekin Chao Fan Ali Mostafavi
author_facet	Qingchun Li Yang Yang Wanqiu Wang Sanghyeon Lee Xin Xiao Xinyu Gao Bora Oztekin Chao Fan Ali Mostafavi
author_sort	Qingchun Li
title	Unraveling the dynamic importance of county-level features in trajectory of COVID-19
title_short	Unraveling the dynamic importance of county-level features in trajectory of COVID-19
title_full	Unraveling the dynamic importance of county-level features in trajectory of COVID-19
title_fullStr	Unraveling the dynamic importance of county-level features in trajectory of COVID-19
title_full_unstemmed	Unraveling the dynamic importance of county-level features in trajectory of COVID-19
title_sort	unraveling the dynamic importance of county-level features in trajectory of covid-19
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ef266603da604585805112e255324a7e
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Unraveling the dynamic importance of county-level features in trajectory of COVID-19

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