Your browser doesn't support javascript.
Association between temperature and COVID-19 transmission in 153 countries.
Liu, Mengyang; Li, Zhiwei; Liu, Mengmeng; Zhu, Yingxuan; Liu, Yue; Kuetche, Mandela William Nzoyoum; Wang, Jianpeng; Wang, Xiaonan; Liu, Xiangtong; Li, Xia; Wang, Wei; Guo, Xiuhua; Tao, Lixin.
  • Liu M; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
  • Li Z; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
  • Liu M; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
  • Zhu Y; Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China.
  • Liu Y; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
  • Kuetche MWN; Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China.
  • Wang J; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
  • Wang X; Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China.
  • Liu X; Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Li X; College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, Xinjiang, Uygur Autonomous Region, People's Republic of China.
  • Wang W; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
  • Guo X; Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China.
  • Tao L; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
Environ Sci Pollut Res Int ; 29(11): 16017-16027, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1460447
ABSTRACT
The WHO characterized coronavirus disease 2019 (COVID-19) as a global pandemic. The influence of temperature on COVID-19 remains unclear. The objective of this study was to investigate the correlation between temperature and daily newly confirmed COVID-19 cases by different climate regions and temperature levels worldwide. Daily data on average temperature (AT), maximum temperature (MAXT), minimum temperature (MINT), and new COVID-19 cases were collected from 153 countries and 31 provinces of mainland China. We used the spline function method to preliminarily explore the relationship between R0 and temperature. The generalized additive model (GAM) was used to analyze the association between temperature and daily new cases of COVID-19, and a random effects meta-analysis was conducted to calculate the pooled results in different regions in the second stage. Our findings revealed that temperature was positively related to daily new cases at low temperature but negatively related to daily new cases at high temperature. When the temperature was below the smoothing plot peak, in the temperate zone or at a low temperature level (e.g., <25th percentiles), the RRs were 1.09 (95% CI 1.04, 1.15), 1.10 (95% CI 1.05, 1.15), and 1.14 (95% CI 1.06, 1.23) associated with a 1°C increase in AT, respectively. Whereas temperature was above the smoothing plot peak, in a tropical zone or at a high temperature level (e.g., >75th percentiles), the RRs were 0.79 (95% CI 0.68, 0.93), 0.60 (95% CI 0.43, 0.83), and 0.48 (95% CI 0.28, 0.81) associated with a 1°C increase in AT, respectively. The results were confirmed to be similar regarding MINT, MAXT, and sensitivity analysis. These findings provide preliminary evidence for the prevention and control of COVID-19 in different regions and temperature levels.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Randomized controlled trials / Reviews Limits: Humans Country/Region as subject: Asia Language: English Journal: Environ Sci Pollut Res Int Journal subject: Environmental Health / Toxicology Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Randomized controlled trials / Reviews Limits: Humans Country/Region as subject: Asia Language: English Journal: Environ Sci Pollut Res Int Journal subject: Environmental Health / Toxicology Year: 2022 Document Type: Article