School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai 200032, China.
Environmental Research Center, Duke Kunshan University, Kunshan, Jiangsu, China; Nicholas School of the Environment, Duke University, Durham, NC, USA.
Sci Total Environ. 2021 Jan 20;753:142272. doi: 10.1016/j.scitotenv.2020.142272. Epub 2020 Sep 9.
To examine the association between meteorological factors (temperature, relative humidity, wind speed, and UV radiation) and transmission capacity of COVID-19.
We collected daily numbers of COVID-19 cases in 202 locations in 8 countries. We matched meteorological data from the NOAA National Centers for Environmental Information. We used a time-frequency approach to examine the possible association between meteorological conditions and basic reproductive number (R) of COVID-19. We determined the correlations between meteorological factors and R of COVID-19 using multiple linear regression models and meta-analysis. We further validated our results using a susceptible-exposed-infectious-recovered (SEIR) metapopulation model to simulate the changes of daily cases of COVID-19 in China under different temperatures and relative humidity conditions.
Temperature did not exhibit significant association with R of COVID-19 (meta p = 0.446). Also, relative humidity (meta p = 0.215), wind speed (meta p = 0.986), and ultraviolet (UV) radiation (meta p = 0.491) were not significantly associated with R either. The SEIR model in China showed that with a wide range of meteorological conditions, the number of COVID-19 confirmed cases would not change substantially.
Meteorological conditions did not have statistically significant associations with the R of COVID-19. Warmer weather alone seems unlikely to reduce the COVID-19 transmission.
研究气象因素(温度、相对湿度、风速和紫外线辐射)与 COVID-19 传播能力的关系。
我们收集了 8 个国家 202 个地点的 COVID-19 每日病例数,并与美国国家海洋和大气管理局(NOAA)国家环境信息中心的气象数据相匹配。我们采用时频方法研究气象条件与 COVID-19 的基本繁殖数(R)之间可能存在的关联。我们使用多元线性回归模型和荟萃分析确定气象因素与 COVID-19 的 R 之间的相关性。我们进一步使用易感-暴露-感染-恢复(SEIR)的元种群模型来验证我们的结果,以模拟不同温度和相对湿度条件下中国 COVID-19 每日病例数的变化。
温度与 COVID-19 的 R 之间没有显著关联(荟萃分析 p=0.446)。相对湿度(荟萃分析 p=0.215)、风速(荟萃分析 p=0.986)和紫外线(UV)辐射(荟萃分析 p=0.491)也与 R 没有显著关联。中国的 SEIR 模型表明,在广泛的气象条件下,COVID-19 确诊病例的数量不会发生实质性变化。
气象条件与 COVID-19 的 R 没有统计学上的显著关联。仅天气变暖似乎不太可能降低 COVID-19 的传播。