Department of Environmental Health, School of Public Health, China Medical University, Shenyang, China.
Department of Occupational Health, School of Public Health, China Medical University, Shenyang, China.
Sci Total Environ. 2020 Aug 1;728:138890. doi: 10.1016/j.scitotenv.2020.138890. Epub 2020 Apr 23.
A COVID-19 outbreak emerged in Wuhan, China at the end of 2019 and developed into a global pandemic during March 2020. The effects of temperature on the dynamics of the COVID-19 epidemic in China are unknown. Data on COVID-19 daily confirmed cases and daily mean temperatures were collected from 31 provincial-level regions in mainland China between Jan. 20 and Feb. 29, 2020. Locally weighted regression and smoothing scatterplot (LOESS), distributed lag nonlinear models (DLNMs), and random-effects meta-analysis were used to examine the relationship between daily confirmed cases rate of COVID-19 and temperature conditions. The daily number of new cases peaked on Feb. 12, and then decreased. The daily confirmed cases rate of COVID-19 had a biphasic relationship with temperature (with a peak at 10 °C), and the daily incidence of COVID-19 decreased at values below and above these values. The overall epidemic intensity of COVID-19 reduced slightly following days with higher temperatures with a relative risk (RR) was 0.96 (95% CI: 0.93, 0.99). A random-effect meta-analysis including 28 provinces in mainland China, we confirmed the statistically significant association between temperature and RR during the study period (Coefficient = -0.0100, 95% CI: -0.0125, -0.0074). The DLNMs in Hubei Province (outside of Wuhan) and Wuhan showed similar patterns of temperature. Additionally, a modified susceptible-exposed-infectious-recovered (M-SEIR) model, with adjustment for climatic factors, was used to provide a complete characterization of the impact of climate on the dynamics of the COVID-19 epidemic.
2019 年底，中国武汉爆发了 COVID-19 疫情，并于 2020 年 3 月发展成为全球大流行。温度对中国 COVID-19 疫情动态的影响尚不清楚。本研究于 2020 年 1 月 20 日至 2 月 29 日期间，从中国内地 31 个省级地区收集了 COVID-19 每日确诊病例和每日平均温度数据。采用局部加权回归和平滑散点图（LOESS）、分布滞后非线性模型（DLNMs）和随机效应荟萃分析来检验 COVID-19 每日确诊病例率与温度条件之间的关系。每日新增病例数于 2 月 12 日达到峰值，随后呈下降趋势。COVID-19 每日确诊病例率与温度呈双相关系（在 10°C 时达到峰值），低于和高于该值时，COVID-19 的日发病率均下降。随着高温日数的增加，COVID-19 的整体流行强度略有下降，相对风险（RR）为 0.96（95%CI：0.93，0.99）。包括中国大陆 28 个省份的随机效应荟萃分析证实了研究期间温度与 RR 之间的统计学显著关联（系数=-0.0100，95%CI：-0.0125，-0.0074）。湖北省（武汉市外）和武汉市的 DLNMs 显示出类似的温度模式。此外，使用修正的易感-暴露-感染-恢复（M-SEIR）模型，调整气候因素，可全面描述气候对 COVID-19 疫情动态的影响。
