Bayesian spatial and spatio-temporal analysis of socioeconomic determinants on COVID-19 mortality.

In this paper, we introduce statistical modeling strategies for assessing the effects of socioeconomic factors such as poverty, income level, and income inequality on COVID-19 mortality across the different phases of the pandemic. In doing so, we consider Bayesian spatial, spatio-temporal and non-spatial models, and discuss relevant inference results. Our findings indicate that deteriorating socioeconomic factors lead to higher mortality rates when we accurately account for spatial effects across neighboring units. In addition, we investigate the effects of temporal variations in socioeconomic covariates on relevant spatial units over time. We provide insights that can be useful for policy makers and public health decision makers. Our numerical analysis focuses on publicly available data merged from various federal as well as state level sources, with an emphasis on the state of California.