Classifying Texas counties using ARIMA Models on COVID-19 daily confirmed cases: the impact of political affiliation and face covering orders

The aim of this paper is to investigate whether the 254 Texas counties in the United States can be grouped in a meaningful way according to the characteristics of the ARIMA or seasonal ARIMA models fitting the logarithm of daily confirmed cases of the Coronavirus Disease 2019 (COVID-19) for 254 counties in Texas of the United States. We analyze clusters of the models non-seasonal parameters (p, d, q), distinguishing between county-level political affiliations and face covering orders, and also consider county-level population and poverty rate. Using data from March 4, 2020 to March 15, 2021, we find that 223 of the total 254 counties are clustered into 23 model parameters (p, d, q), while the number of cases in the remaining 31 counties could not be successfully fitted to ARIMA models. We also find the impact of the county-level infection rate and the county-level poverty rate on clusters of counties with different political affiliations and face covering orders. Further, we find that the infection rate and the poverty rate had a significant high positive correlation, and Democrat-leaning counties, which tend to have large populations, had a higher correlation coefficient between infection rate and poverty rate. We also observe a significant high positive correlation between the infection rate and the number of cumulative cases in Republican counties that had not imposed a face covering order.