Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID-19 in the Conterminous United States

Abstract

This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (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, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID-19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States 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, having non-White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID-19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID-19 cases, and COVID-19 deaths.

Keywords: Bayesian spatiotemporal disease modeling; COVID‐19 pandemic; environmental determinants of COVID‐19; remote sensing and COVID‐19; social vulnerability; spatial epidemiology.

Figure 1

(a) Cases of coronavirus disease 2019 (COVID‐19) per 100,000 count of population in the United States for the year 2020 (Crimson line = Locally weighted scatterplot smoothing (LOWESS) line of cases). (b) Deaths from COVID‐19 per 100,000 count of population in the United States for the year 2020 (Crimson line = Locally weighted scatterplot smoothing (LOWESS) line of deaths).

Figure 2

Posterior temporally structured; γ _t (light blue) and unstructured; ω _t (light red) effects for coronavirus disease 2019 cases; panel (a), and deaths; panel (b) (95% credibility envelope) in the United States by month for the year 2020.

Figure 3

Exceedance probabilities of convolutional spatial effect ($e^{\left(S_{\ast }+U_{\ast }\right)}$) and spatially structured effect ($e^{\left(S_{\ast }\right)}$) relative risk associated with coronavirus disease 2019 (COVID‐19) infection; panel (a), and COVID‐19 mortality; panel (b).

Figure 4

Probabilities of space‐time interaction term $\mathrm{Pr}\left(e^{{\delta }_{it}}\right)$ exceeding 1.00 during the year 2020 for United States coronavirus disease 2019 infections stratified monthly.

Figure 5

Probabilities of space‐time interaction term $\mathrm{Pr}\left(e^{{\delta }_{it}}\right)$ exceeding 1.00 during the year 2020 for United States. Coronavirus disease 2019 mortalities stratified monthly.

Figure 6

Boxplot of Social Vulnerability Index composite score for hot and cold spots for the convolutional spatial effect for coronavirus disease 2019 (COVID‐19) infections; plot (a), COVID‐19 fatalities; plot (b).

Figure 7

Boxplots organized by month, comparing cold spots and hot spots of coronavirus disease 2019 cases (a) and deaths (b) to composite Social Vulnerability Index score.