Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, Las Vegas, NV, USA.
Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, Las Vegas, NV, USA.
J Expo Sci Environ Epidemiol. 2022 Sep;32(5):774-781. doi: 10.1038/s41370-021-00356-y. Epub 2021 Jul 1.
The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory.
The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States.
This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation.
Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States.
The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.
全球范围内都在讨论气象因素与 2019 年冠状病毒病（COVID-19）之间的关联，但由于研究时间短、缺乏对滞后效应的考虑以及研究区域不同，文献中的结果存在差异，甚至相互矛盾。
本研究的主要目的是通过考虑较长的研究期和美国多样化的高风险地区，进行更可靠的研究，以评估气象因素对 COVID-19 发病率的滞后影响。
本研究采用具有空间函数的分布式滞后非线性模型，分析了 2020 年 3 月至 10 月美国 203 个高风险县的多种气象措施对 COVID-19 发病率的预测。估计的空间函数通过双调和样条插值在整个美国大陆进行了进一步平滑。
研究结果表明，最高温度、最低相对湿度和降水量是最佳的气象预测指标。在每个选定气象因素的较低水平下，滞后 3 至 11 天的滞后时间与 COVID-19 发病率之间存在最显著的正相关关系。特别是在滞后 5 天，相对湿度高于 88.36%时，出现了显著的正相关关系。空间分析还显示，美国中北部地区存在过度风险。
研究结果可以为高风险县使用天气预报提前两周实施 COVID-19 预警监测提供参考。
