Spatiotemporal Associations Between Social Vulnerability, Environmental
Measurements, and COVID-19 in the Conterminous United States
Abstract
This study introduces the results from fitting a Bayesian hierarchical
spatiotemporal model to COVID-19 cases and deaths at the county-level in
the United States for the year 2020. Two models were created, one for
cases and one for deaths, utilizing a scaled Besag, York, MolliƩ model
with Type I spatial-temporal interaction. Each model accounts for 16
social vulnerability variables and 7 environmental measurements as fixed
effects. The spatial structure of COVID-19 infections is heavily focused
in the southern U.S. and the states of Indiana, Iowa, and New Mexico.
The spatial structure of COVID-19 deaths covers less of the same area
but also encompasses a cluster in the Northeast. The spatiotemporal
trend of the pandemic in the U.S. illustrates a shift out of many of the
major metropolitan areas into the U.S. Southeast and Southwest during
the summer months and into the upper Midwest beginning in autumn.
Analysis of the major social vulnerability predictors of COVID-19
infection and death found that counties with higher percentages of those
not having a high school diploma and having minority status to be
significant. Age 65 and over was a significant factor in deaths but not
in cases. Among the environmental variables, above ground level (AGL)
temperature had the strongest effect on relative risk to both cases and
deaths. Hot and cold spots of COVID-19 cases and deaths derived from the
convolutional spatial effect show that areas with a high probability of
above average relative risk have significantly higher SVI composite
scores. Hot and cold spot analysis utilizing the spatiotemporal
interaction term exemplifies a more complex relationship between social
vulnerability, environmental measurements, and cases/deaths.