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Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina

Author

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  • Tianchu Lyu

    (Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
    Equal contribution.)

  • Nicole Hair

    (Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
    Equal contribution.)

  • Nicholas Yell

    (Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA)

  • Zhenlong Li

    (Department of Geography, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA)

  • Shan Qiao

    (Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA)

  • Chen Liang

    (Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA)

  • Xiaoming Li

    (Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA)

Abstract

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal–geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection ( p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal–geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.

Suggested Citation

  • Tianchu Lyu & Nicole Hair & Nicholas Yell & Zhenlong Li & Shan Qiao & Chen Liang & Xiaoming Li, 2021. "Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina," IJERPH, MDPI, vol. 18(18), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9673-:d:635197
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    References listed on IDEAS

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    1. Ziyad Al-Aly & Yan Xie & Benjamin Bowe, 2021. "High-dimensional characterization of post-acute sequelae of COVID-19," Nature, Nature, vol. 594(7862), pages 259-264, June.
    2. George B Cunningham & Lisa T Wigfall, 2020. "Race, explicit racial attitudes, implicit racial attitudes, and COVID-19 cases and deaths: An analysis of counties in the United States," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    3. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    4. Terrie Walmsley & Adam Rose & Dan Wei, 2021. "The Impacts of the Coronavirus on the Economy of the United States," Economics of Disasters and Climate Change, Springer, vol. 5(1), pages 1-52, April.
    5. Moehring, Katja & Weiland, Andreas & Reifenscheid, Maximiliane & Naumann, Elias & Wenz, Alexander & Rettig, Tobias & Krieger, Ulrich & Fikel, Marina & Cornesse, Carina & Blom, Annelies G., 2021. "Inequality in employment trajectories and their socio-economic consequences during the early phase of the COVID-19 pandemic in Germany," SocArXiv m95df, Center for Open Science.
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    2. Krzysztof Rząsa & Mateusz Ciski, 2022. "Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression," IJERPH, MDPI, vol. 19(19), pages 1-26, September.

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