Qin Lei, Sun Qiang, Shao Jiani, Chen Yang, Zhang Xiaomei, Li Jian, Chen Mingchih, Shia Ben-Chang, Wu Szu-Yuan
School of Statistics, University of International Business and Economics Beijing, China.
School of International Education, University of International Business and Economics Beijing, China.
Am J Transl Res. 2021 Jun 15;13(6):5943-5955. eCollection 2021.
The effects of temperature and relative humidity on the growth of coronavirus disease 2019 (COVID-19) remain unclear. Data on the COVID-19 epidemic that were analyzed in this study were obtained from the official websites of the National Health Commission of China and the Health Commissions of 31 provinces in China. From January 26 to February 25, 2020, the cumulative number of confirmed COVID-19 cases in each region was counted daily using data from our database. Curve fitting of daily scatter plots of the relationship between epidemic growth rate (GR) with average temperature (AT) and average relative humidity (ARH) was conducted using the loess method. The heterogeneity across days and provinces was calculated to assess the necessity of using a longitudinal model. Fixed-effect models with polynomial terms were developed to quantify the relationship between variations in the GR and AT or ARH. An increased AT markedly reduced the GR when the AT was lower than -5°C, the GR was moderately reduced when the AT ranged from -5°C to 15°C, and the GR increased when the AT exceeded 15°C. ARH increased the GR when it was less than 72% and reduced the GR when it exceeded 72%. The temperature and relative humidity curves were not linearly associated with the GR of COVID-19. The GR was moderately reduced when the AT ranged from -5°C to 15°C. When the AT was lower or higher than -5°C to 15°C, the GR of COVID-19 increased. An increased ARH increased the GR when the ARH was lower than 72% and reduced the GR when the ARH exceeded 72%.
温度和相对湿度对2019冠状病毒病（COVID-19）生长的影响仍不清楚。本研究中分析的COVID-19疫情数据来自中国国家卫生健康委员会和中国31个省份卫生健康委员会的官方网站。2020年1月26日至2月25日，每天使用我们数据库中的数据统计各地区COVID-19确诊病例的累计数量。采用局部加权回归散点平滑法（loess方法）对疫情增长率（GR）与平均温度（AT）和平均相对湿度（ARH）之间关系的每日散点图进行曲线拟合。计算不同日期和省份之间的异质性，以评估使用纵向模型的必要性。建立了带有多项式项的固定效应模型，以量化GR变化与AT或ARH之间的关系。当AT低于-5°C时，AT升高会显著降低GR；当AT在-5°C至15°C之间时，GR会适度降低；当AT超过15°C时，GR会升高。当ARH低于72%时，ARH升高会增加GR；当ARH超过72%时，ARH升高会降低GR。温度和相对湿度曲线与COVID-19的GR并非线性相关。当AT在-5°C至15°C之间时，GR会适度降低。当AT低于或高于-5°C至15°C时，COVID-19的GR会升高。当ARH低于72%时，ARH升高会增加GR；当ARH超过72%时，ARH升高会降低GR。
