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.

中文翻译：

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美国本土社会脆弱性、环境测量和 COVID-19 之间的时空关联

本研究总结了贝叶斯分层时空模型与 2020 年美国县级 2019 年冠状病毒病 (COVID-19) 病例和死亡人数的拟合结果。创建了两个模型，一个用于病例，一个用于死亡，利用具有 I 型时空交互作用的缩放 Besag、York、Mollié 模型。每个模型都考虑了 16 个社会脆弱性和 7 个环境变量作为固定效应。COVID-19 病例和死亡人数之间的空间模式在很多方面都存在显着差异。美国疫情的时空趋势表明，在夏季，疫情从许多主要大都市区转移到美国东南部和西南部，并从秋季开始转移到中西部北部。对 COVID-19 感染和死亡的主要社会脆弱性预测因素的分析发现，没有高中文凭、非白人身份和 65 岁及以上人口比例较高的县非常重要。在环境变量中，地面温度对病例和死亡的相对风险影响最大。根据卷积空间效应得出的热点和冷点、COVID-19 病例和死亡人数分别具有统计显着性高和低的区域表明，相对风险高于平均水平的可能性较高的区域具有显着较高的社会脆弱性指数综合得分。利用时空相互作用术语的同一分析例证了社会脆弱性、环境测量、COVID-19 病例和 COVID-19 死亡之间更复杂的关系。