The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
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