The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.
The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.
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平均气温和相对湿度对新冠肺炎新发病例影响的时间序列分析
DOI：
10.2196/20495
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发表时间：
2021-01-25
影响因子：
8.5
通讯作者：
Ming WK
中科院分区：
文献类型：
--
作者：
He Z;Chin Y;Yu S;Huang J;Zhang CJP;Zhu K;Azarakhsh N;Sheng J;He Y;Jayavanth P;Liu Q;Akinwunmi BO;Ming WK
气象因素对COVID-19传播和扩散的影响值得关注，但尚未进行调查。本研究旨在调查9个亚洲城市的气象因素与COVID-19每日新增病例数之间的关系。我们采用皮尔逊相关和广义加性模型（GAM），以目前最新的数据评估每日新增COVID-19病例与气象因素（每日平均温度和相对湿度）之间的关系。皮尔逊相关性显示，每日新确诊的COVID-19病例与平均气温的相关性大于与相对湿度的相关性。北京、上海和广州的日新增确诊病例数与平均气温呈负相关（r=-0.565，P <.001），上海和广州的日新增确诊病例数与平均气温呈负相关（r=-0.47，P <.001），广州的日新增确诊病例数与平均气温呈负相关（r=-0.53，P <.001）。然而，在日本，观察到正相关（r=0.416，P<.001）。在大多数城市（上海、广州、香港、首尔、东京和吉隆坡），GAM分析显示每日新增确诊病例数与平均温度和相对湿度呈正相关，特别是使用滞后3D建模时，在5个城市（北京、武汉、韩国和马来西亚除外）发现温度对每日新增确诊病例有积极影响。此外，敏感性分析表明，通过将城市等级和公共卫生措施纳入模型，在滞后的3天模型中，较高的温度可以增加每日新发病例数（β =0.073，Z=11.594，P<.001）。研究结果表明，增加的温度产量增加了每日新的COVID-19病例。因此，在广泛提供疫苗和建立群体免疫力之前，仍然需要采取大规模的公共卫生措施和扩大区域研究。
The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model. The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.
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DOI：
10.1038/nrmicro.2016.81
发表时间：
2016-08
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
de Wit E;van Doremalen N;Falzarano D;Munster VJ
通讯作者：
Munster VJ
影响因子：
4.7
作者：
Gralinski, Lisa E.;Menachery, Vineet D.
通讯作者：
Menachery, Vineet D.
影响因子：
3.7
作者：
Dublineau, Amelie;Batejat, Christophe;Manuguerra, Jean-Claude
通讯作者：
Manuguerra, Jean-Claude
影响因子：
--
作者：
Chan, Ken;Sewell, Philip;Benson, Trevor
通讯作者：
Benson, Trevor
影响因子：
11.8
作者：
Kan, Haidong;London, Stephanie J.;Chen, Bingheng
通讯作者：
Chen, Bingheng