The world is experiencing a pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The USA is also suffering from a catastrophic death toll from COVID-19. Several studies are providing preliminary evidence that short- and long-term exposure to air pollution might increase the severity of COVID-19 outcomes, including a higher risk of death. In this study, we develop a spatiotemporal model to estimate the association between exposure to fine particulate matter PM2.5 and mortality accounting for several social and environmental factors. More specifically, we implement a Bayesian zero-inflated negative binomial regression model with random effects that vary in time and space. Our goal is to estimate the association between air pollution and mortality accounting for the spatiotemporal variability that remained unexplained by the measured confounders. We applied our model to four regions of the USA with weekly data available for each county within each region. We analyze the data separately for each region because each region shows a different disease spread pattern. We found a positive association between long-term exposure to PM2.5 and the mortality from the COVID-19 disease for all four regions with three of four being statistically significant. Data and code are available at our GitHub repository. Supplementary materials accompanying this paper appear on-line.

由于严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2)，也称为 COVID-19，世界正在经历一场大流行。美国也因 COVID-19 造成灾难性死亡人数。几项研究提供了初步证据，表明短期和长期暴露于空气污染可能会增加 COVID-19 结果的严重性，包括更高的死亡风险。在这项研究中，我们开发了一个时空模型来估计暴露于细颗粒物 PM2.5 与死亡率之间的关联，并解释了几个社会和环境因素。更具体地说，我们实现了贝叶斯零膨胀负二项式回归模型，该模型具有随时间和空间变化的随机效应。我们的目标是估计空气污染与死亡率之间的关联，以解释测量的混杂因素无法解释的时空变异性。我们将我们的模型应用于美国的四个地区，每个地区的每个县都有每周可用的数据。我们分别分析每个地区的数据，因为每个地区显示出不同的疾病传播模式。我们发现，在所有四个地区，长期暴露于 PM2.5 与 COVID-19 疾病的死亡率之间存在正相关关系，其中四个地区中有三个具有统计学意义。数据和代码可在我们的 GitHub 存储库中获得。本文随附的补充材料出现在网上。我们分别分析每个地区的数据，因为每个地区显示出不同的疾病传播模式。我们发现，在所有四个地区，长期暴露于 PM2.5 与 COVID-19 疾病的死亡率之间存在正相关关系，其中四个地区中有三个具有统计学意义。数据和代码可在我们的 GitHub 存储库中获得。本文随附的补充材料出现在网上。我们分别分析每个地区的数据，因为每个地区显示出不同的疾病传播模式。我们发现，在所有四个地区，长期暴露于 PM2.5 与 COVID-19 疾病的死亡率之间存在正相关关系，其中四个地区中有三个具有统计学意义。数据和代码可在我们的 GitHub 存储库中获得。本文随附的补充材料出现在网上。