The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM_2.5) and ozone (O₃), atmospheric pollutants related to people’s mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM_2.5 and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources.

中文翻译：

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纽约州九个县新的 COVID-19 病例与空气污染与气象要素之间的关联


本文的主要目的是评估 COVID-19 感染病例数与细颗粒物 (PM _2.5 ) 和臭氧 (O ₃ )（与城市地区人口流动相关的大气污染物）浓度之间可能存在的关联，采取还要考虑气象条件的影响。我们拟合了一个包含空间和时间项的广义线性混合模型，以检测气象因素和 COVID-19 感染病例对污染物浓度的影响。我们考虑了纽约州登记的 COVID-19 感染病例数最多的 9 个县。我们使用集成嵌套拉普拉斯近似 (INLA) 和随机偏微分方程 (SPDE) 实现了贝叶斯方法。结果强调，设计模型时使用的所有组件都有助于提高预测值，并且可以包含在设计类似的真实世界数据 (RWD) 模型中。我们发现 PM _2.5和臭氧浓度与 COVID-19 感染病例之间只有微弱的关联。 2020 年 3 月至 5 月的 COVID-19 感染病例记录和其他协变量数据是从电子健康记录 (EHR) 和标准 RWD 来源收集的。