Association between ambient temperature and COVID-19 infection in 122 cities from China
By Jingui Xie, Yongjian Zhu. Science of the Total Environment, 2020
779 citations recorded by the source corpus.
Background Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus. Methods Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail. Results The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0–14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209–6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses. Conclusions Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.
Subfields: Global and Planetary Change, Health, Toxicology and Mutagenesis, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/66a25df46d59f2eba0cff8871d354c4080709cf0
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Citations in the corpus
- Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries (2020, neutral): “Epidemiological studies have explored the relationship between COVID-19 and meteorological parameters; however, findings are controversial (Ma et al., 2020; Xie and Zhu, 2020; Yao et al., 2020).”
- Neural Networks Fail to Learn Periodic Functions and How to Fix It (2020, background): “mportant for medical applications. Many diseases and epidemics are known to have strong correlation with atmospheric temperature, such as SARS [6] and the current COVID-19 crisis ongoing in the world [44, 36, 27]. Therefore, being able to model temperature accurately could be important for related p”