The novel SARS-CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS-CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio-temporal analysis in Spain of the incidence of COVID-19, the disease caused by the virus. Use of spatial Seemingly Unrelated Regressions (SUR) allows us to model the incidence of reported cases of the disease per 100,000 population as an interregional contagion process, in addition to a function of temperature, humidity, and sunshine. In the analysis we also control for GDP per capita, percentage of older adults in the population, population density, and presence of mass transit systems. The results support the hypothesis that incidence of the disease is lower at higher temperatures and higher levels of humidity. Sunshine, in contrast, displays a positive association with incidence of the disease. Our control variables also yield interesting insights. Higher incidence is associated with higher GDP per capita and presence of mass transit systems in the province; in contrast, population density and percentage of older adults display negative associations with incidence of COVID-19.

中文翻译：

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西班牙 COVID-19 发病的环境相关性的时空分析

新型 SARS-CoV2 扰乱了卫生系统和经济，减缓其传播的公共卫生干预措施成本高昂。如何以及何时放宽对行动的限制，部分取决于 SARS-CoV2 是否会因温度、湿度和日照时间的变化而表现出季节性。在这里，我们通过在西班牙对由病毒引起的疾病 COVID-19 的发病率进行时空分析来解决这个问题。除了温度、湿度和阳光的函数外，使用空间看似无关的回归 (SUR) 使我们能够将每 100,000 人口中报告的疾病病例的发病率建模为区域间传染过程。在分析中，我们还控制了人均 GDP、人口中老年人的百分比、人口密度和公共交通系统的存在。结果支持这样的假设，即在较高温度和较高湿度水平下该疾病的发病率较低。相比之下，阳光与疾病的发病率呈正相关。我们的控制变量也产生了有趣的见解。较高的发病率与较高的人均 GDP 和该省公共交通系统的存在有关；相比之下，人口密度和老年人百分比与 COVID-19 的发病率呈负相关。较高的发病率与较高的人均 GDP 和该省公共交通系统的存在有关；相比之下，人口密度和老年人百分比与 COVID-19 的发病率呈负相关。较高的发病率与较高的人均 GDP 和该省公共交通系统的存在有关；相比之下，人口密度和老年人百分比与 COVID-19 的发病率呈负相关。