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

Author

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  • Han Yue

    (Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Tao Hu

    (Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA
    Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA)

Abstract

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.

Suggested Citation

  • Han Yue & Tao Hu, 2021. "Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States," IJERPH, MDPI, vol. 18(13), pages 1-16, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:13:p:6832-:d:582281
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    References listed on IDEAS

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    1. Michael F. Goodchild & Robert P. Haining, 2004. "GIS and spatial data analysis: Converging perspectives," Advances in Spatial Science, in: Raymond J. G. M. Florax & David A. Plane (ed.), Fifty Years of Regional Science, pages 363-385, Springer.
    2. Jixia Huang & Jinfeng Wang & Yanchen Bo & Chengdong Xu & Maogui Hu & Dacang Huang, 2014. "Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique," IJERPH, MDPI, vol. 11(3), pages 1-17, March.
    3. Luc Anselin & Yong Wook Kim & Ibnu Syabri, 2004. "Web-based analytical tools for the exploration of spatial data," Journal of Geographical Systems, Springer, vol. 6(2), pages 197-218, June.
    4. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
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    Cited by:

    1. Xiaotong Gao & Naigang Cao & Yushuo Zhang & Lin Zhao, 2022. "Spatial Structure of China’s Green Development Efficiency: A Perspective Based on Social Network Analysis," Sustainability, MDPI, vol. 14(23), pages 1-16, December.
    2. Chan Chen & Jie Li & Jian Huang, 2022. "Spatial–Temporal Patterns of Population Aging in Rural China," IJERPH, MDPI, vol. 19(23), pages 1-18, November.

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