Impact of temperature on the dynamics of the COVID-19 outbreak in China
Author
Shi, Peng
Dong, Yinqiao
Yan, Huanchang
Zhao, Chenkai
Li, Xiaoyang
Liu, Wei
He, Miao
Tang, Shixing
Xi, Shuhua
Institutions
Abstract
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 metaanalysis 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) andWuhan 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.