Finnish Meteorological Institute, Meteorological and Marine Research Programme, Weather and Climate Change Impact Research, P.O. Box 503, 00101 Helsinki, Finland.
The Center of Statistics, University of Turku, 20500 Turku, Finland.
Int J Environ Res Public Health. 2022 Oct 17;19(20):13398. doi: 10.3390/ijerph192013398.
We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.
我们对 2020 年 8 月至 2021 年 5 月芬兰南部赫尔辛基和乌西玛地区医院区的每日新增冠状病毒病 2019(COVID-19)病例数与选定气象因素的关系进行建模。我们应用了带有和不带有各种环境和非环境混杂因素的分布式滞后非线性模型(DLNM)。每日平均温度或绝对湿度与 COVID-19 发病率之间的关系呈非线性依赖性,在 0 至-10°C 的低温或绝对湿度(AH)值低于 6 g/m 的低温下,COVID-19 的发病率增加。然而,需要谨慎解释这些结果,因为发现的关联可能仅在 2020-2021 年的研究期间有效。需要更长的研究周期来研究严重急性呼吸系统综合症冠状病毒 2(SARS-CoV-2)是否具有类似于流感和其他病毒性呼吸道感染的季节性模式。在未来的研究中,需要考虑其他非环境因素(如各种缓解措施)的影响。了解气象因素与 COVID-19 之间的关联可以为决策者以及教育和卫生部门提供有用的信息,以便预测和准备未来冬季的疫情浪潮。