Purpose: There are growing signs that the COVID‐19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county‐level change in prevalence rates of COVID‐19 by rural‐urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates.Methods: We used crowdsourced data on COVID‐19 and linked them to county‐level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R‐studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis‐Ord local statistics.Findings: In the rural counties, the mean prevalence of COVID‐19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID‐19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID‐19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25‐49 years had increased COVID‐19 prevalence rates.Conclusions: Our findings show a rapid spread of COVID‐19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID‐19 prevalence.

Purpose: To identify the county‐level effects of social determinants of health (SDoH) on COVID‐19 (corona virus disease 2019) mortality rates by rural–urban residence and estimate county‐level exceedance probabilities for detecting clusters.Methods: The county‐level data on COVID‐19 death counts as of October 23, 2020, were obtained from the Johns Hopkins University. SDoH data were collected from the County Health Ranking and Roadmaps, the US Department of Agriculture, and the Bureau of Labor Statistics. Semiparametric negative binomial regressions with expected counts based on standardized mortality rates as offset variables were fitted using integrated Laplace approximation. Bayesian significance was assessed by 95% credible intervals (CrI) of risk ratios (RR). County‐level mortality hotspots were identified by exceedance probabilities.Findings: The COVID‐19 mortality rates per 100,000 were 65.43 for the urban and 50.78 for the rural counties. Percent of Blacks, HIV, and diabetes rates were significantly associated with higher mortality in rural and urban counties, whereas the unemployment rate (adjusted RR = 1.479, CrI = 1.171, 1.867) and residential segregation (adjusted RR = 1.034, CrI = 1.019, 1.050) were associated with increased mortality in urban counties. Counties with a higher percentage of college or associate degrees had lower COVID‐19 mortality rates.Conclusions: SDoH plays an important role in explaining differential COVID‐19 mortality rates and should be considered for resource allocations and policy decisions on operational needs for businesses and schools at county levels.

Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level—where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space–time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

Social determinants of health and racial inequalities impact healthcare access and subsequent coronavirus testing. Limited studies have described the impact of these inequities on rural minorities living in Appalachia. This study investigates factors affecting testing in rural communities. PCR testing data were obtained for March through September 2020. Spatial regression analyses were fit at the census tract level. Model outcomes included testing and positivity rate. Covariates included rurality, percent Black population, food insecurity, and area deprivation index (a comprehensive indicator of socioeconomic status). Small clusters in coronavirus testing were detected sporadically, while test positivity clustered in mideastern and southwestern WV. In regression analyses, percent food insecurity (IRR = 3.69×10⁹, [796, 1.92×10¹⁶]), rurality (IRR=1.28, [1.12, 1.48]), and percent population Black (IRR = 0.88, [0.84, 0.94]) had substantial effects on coronavirus testing. However, only percent food insecurity (IRR = 5.98 × 10⁴, [3.59, 1.07×10⁹]) and percent Black population (IRR = 0.94, [0.90, 0.97]) displayed substantial effects on the test positivity rate. Findings highlight disparities in coronavirus testing among communities with rural minorities. Limited testing in these communities may misrepresent coronavirus incidence.