Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 coun...

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Main Authors: Han Yue, Tao Hu
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/18/13/6832
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spelling doaj-dee9c5575bd1408c9fd5d689001f3bfa2021-07-15T15:34:40ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012021-06-01186832683210.3390/ijerph18136832Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United StatesHan Yue0Tao Hu1Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaDepartment of Geography, Oklahoma State University, Stillwater, OK 74078, USAInvestigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.https://www.mdpi.com/1660-4601/18/13/6832COVID-19geographical detectorspatial distributionimpact factorinteractive effect
collection DOAJ
language English
format Article
sources DOAJ
author Han Yue
Tao Hu
spellingShingle Han Yue
Tao Hu
Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
International Journal of Environmental Research and Public Health
COVID-19
geographical detector
spatial distribution
impact factor
interactive effect
author_facet Han Yue
Tao Hu
author_sort Han Yue
title Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_short Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_full Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_fullStr Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_full_unstemmed Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_sort geographical detector-based spatial modeling of the covid-19 mortality rate in the continental united states
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1661-7827
1660-4601
publishDate 2021-06-01
description Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.
topic COVID-19
geographical detector
spatial distribution
impact factor
interactive effect
url https://www.mdpi.com/1660-4601/18/13/6832
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