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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.

Introduction: Disparities and their geospatial patterns exist in coronavirus disease 2019 (COVID-19) morbidity and mortality for people who are engaged with clinical care. However, studies centered on viral infection cases are scarce. It remains unclear with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs) for people with the infection. This work aimed to assess the geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina by different timepoints during the pandemic. Method: We used global models including spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR), as well as geographically weighted regression model (GWR) as a local model to examine the associations between COVID-19 infection rate and PIDRs. The data were retrieved from multiple sources including USAFacts, US Census Bureau, and Population Estimates Program. Results: The percentage of males and the percentage of the unemployed population were statistically significant (p values < 0.05) with positive coefficients in the three global models (SEM, SLM, CAR) throughout the time. The percentage of white population and obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models consistently have a better model fit than global models, suggesting non-stationary correlations between a region and its neighbors. Conclusion: Characterized by temporal-geospatial patterns, disparities and their PIDRs exist in COVID-19 incidence at the county level in South Carolina. The temporal-geospatial structure of disparities and their PIDRs found in COVID-19 incidence are different from mortality and morbidity for patients who are connected with clinical care. Our findings provided important evidence for prioritizing different populations and developing tailored interventions at different times of the pandemic. These findings provided implications on containing early viral transmission and mitigating consequences of infectious disease outbreaks for possible future pandemics.