tetano
Editor, Senior Moderator
Environ Sci Pollut Res Int
. 2022 Jan 30.
doi: 10.1007/s11356-021-18382-6. Online ahead of print.
The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study
Zheng-Gang Fang[SUP] 1 [/SUP], Shu-Qin Yang[SUP] 1 [/SUP], Cai-Xia Lv[SUP] 1 [/SUP], Shu-Yi An[SUP] 2 [/SUP], Peng Guan[SUP] 1 [/SUP], De-Sheng Huang[SUP] 3 [/SUP], Bao-Sen Zhou[SUP] 1 [/SUP], Wei Wu[SUP] 4 [/SUP]
Affiliations
Abstract
The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.
Keywords: Distributed lag nonlinear models; Imported risk; Novel coronavirus disease; Temperature.
. 2022 Jan 30.
doi: 10.1007/s11356-021-18382-6. Online ahead of print.
The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study
Zheng-Gang Fang[SUP] 1 [/SUP], Shu-Qin Yang[SUP] 1 [/SUP], Cai-Xia Lv[SUP] 1 [/SUP], Shu-Yi An[SUP] 2 [/SUP], Peng Guan[SUP] 1 [/SUP], De-Sheng Huang[SUP] 3 [/SUP], Bao-Sen Zhou[SUP] 1 [/SUP], Wei Wu[SUP] 4 [/SUP]
Affiliations
- PMID: 35094276
- DOI: 10.1007/s11356-021-18382-6
Abstract
The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.
Keywords: Distributed lag nonlinear models; Imported risk; Novel coronavirus disease; Temperature.