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首页> 外文期刊>JMIR public health and surveillance. >The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis
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The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis

机译：平均温度和相对湿度对Covid-19新病例的影响：时间序列分析

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BACKGROUND:The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been elucidated.OBJECTIVE:To investigate the associations of meteorological factors and the daily new cases of coronavirus disease (COVID-19) in nine Asian cities.METHODS:Pearson's 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.RESULTS: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=.565, P.01), Shanghai (r=-.471, P.01), and Guangzhou (r=-.530, P.01) , yet in contrast, positively correlated with that in Japan (r=.441, P.01). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), generalized additive modeling analysis showed the number of daily new confirmed cases was positively associated with both average temperature and relative humidity, especially in lagged 3d model, where a positive influence of temperature on the daily new confirmed cases was discerned in 5 cities except in Beijing, Wuhan, Korea, and Malaysia. Moreover, sensitivity analysis by incorporating the city grade and public health measures into the model showed that high temperature can increase the daily new cases (Beta=0.073, Z=11.594, P0.001) in lagged 3d model.CONCLUSIONS:With increased temperature, the daily new cases of COVID-19 increases. Large-scale public health measures and expanded regional research are still required until a vaccine becomes available and herd immunity is established.
机译：背景：气象因素对Covid-19的传播和传播的影响，尚未阐明。目的：调查气象因素和日常冠状病毒病（Covid-19）的日常新病例的九个亚洲在城市。方法：Pearson的相关性和广义添加剂建模（Gam）进行了评估日常新的Covid-19病例与气象因素（日均温度和相对湿度）之间的关系，具有当前可用的最新数据。结果：Pearson相关性表明，每日新的Covid-19案例与平均温度比相对湿度更加相关。每日新的确诊病例与北京的平均气温呈负相关（R = .565，P＆ .01），上海（R = - 。471，P＆ 01）和广州（r = - 。530，p＆然而，然而，相反，与日本的相比之下（r = .441，p＆ .01）呈正相关。在大多数城市（上海，广州，香港，首尔，东京和吉隆坡）中，广义添加剂建模分析显示日常新确诊病例的数量与平均温度和相对湿度正相关，特别是在滞后3D模型中除北京，武汉，韩国和马来西亚外，5个城市，在5个城市中辨别出日常新确诊病例的积极影响。此外，通过将城市等级和公共卫生措施纳入模型的敏感性分析表明，在滞后的3D模型中，高温可以增加日常新病例（β= 0.073，Z = 11.594，P <0.001）。结论：随着温度的增加， Covid-19的日常新案例增加。仍需要大规模的公共卫生措施和扩大的区域研究，直到疫苗可用，并建立畜群免疫力。

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