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Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties

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  • Vivian Yi-Ju Chen
  • Kiwoong Park
  • Feinuo Sun
  • Tse-Chuan Yang

Abstract

Purpose: Research on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks. Methods: Analyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups—High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk—and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors. Results: The key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana. Conclusions: The COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.

Suggested Citation

  • Vivian Yi-Ju Chen & Kiwoong Park & Feinuo Sun & Tse-Chuan Yang, 2022. "Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-16, April.
    • Handle: RePEc:plo:pone00:0265673
    DOI: 10.1371/journal.pone.0265673
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    References listed on IDEAS

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    1. Clouston, Sean A.P. & Natale, Ginny & Link, Bruce G., 2021. "Socioeconomic inequalities in the spread of coronavirus-19 in the United States: A examination of the emergence of social inequalities," Social Science & Medicine, Elsevier, vol. 268(C).
    2. Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166.
    3. Alan Agresti & Claudia Tarantola, 2018. "Simple ways to interpret effects in modeling ordinal categorical data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 210-223, August.
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