COVID-19: High temperature and high humidity reduce the transmission of COVID-19.
COVID-19: High temperature and high humidity reduce the transmission of COVID-19. Spencer EA, Heneghan C.
https://www.cebm.net/study/covid-19-high-temperature-and-high-humidity-reduce-the-transmission-of-covid-19/
Published on July 2, 2020
Included in
Transmission Dynamics of COVID-19
| Reference |
Wang J, Tang K, Feng K, Lin X, Weifeng L, Chen K, Wang F. High temperature and high humidity reduce the transmission of COVID-19 Available at SSRN: https://ssrn.com/abstract=3551767 2020 |
| Study type |
|
| Country |
China and USA |
| Setting |
Global |
| Funding Details |
Non reported |
| Transmission mode |
Meteorological, Urban environment |
| Exposures |
Temperature, Relative humidity, density |
Bottom Line
Some of the variations in COVID-19 transmission between cities worldwide can be explained by higher temperature and humidity being associated with lower transmission rates.
Evidence Summary
69,498 cases in China and 740,843 in the USA were included in the analysis.
- A 1°C increase in temperature was associated with a reduction in R of 0.023 (95% CI [-0.0395 to -0.0125) in China and 0.020 (95% CI -0.0311 to -0.0096) in the US.
- A 1% relative humidity rise was associated with a reduction in RE by 0.0078 (95% CI -0.011 to -0.0045) in China and 0.0080 (95% CI -0.015 to -0.0010) in the US.
After the lockdowns in China and the US, temperature and relative humidity were still associated with changes in transmission but appeared to have a lower impact.
A one thousand people per square kilometre rise in population density was associated with a 0.12 (95% CI [0.057, 0.18]) increase in the R-value before lockdown.
What did they do?
Cases with symptom-onset dates 19th January to 10th February 2020 for 100 Chinese cities, and cases with confirmed dates from 15th March to 25th April for 1,005 US counties.
The authors assessed the relationship between the transmissibility of COVID-19 and the temperature/humidity, controlling for various demographic, socioeconomic, geographic, healthcare and policy factors and correcting for cross-sectional correlation.
Data sources
- Temperature and relative humidity data are obtained from http://data.cma.cn/.
- Population density, from https://data.cnki.net.
- Baidu Mobility Indexes are obtained from https://qianxi.baidu.com/.
- US temperature and relative humidity data are from National Oceanic and Atmospheric Administration at https://www.ncdc.noaa.gov/.
- Population data are obtained from https://www.census.gov/.
- GDP and person income in 2018 for each county are obtained from https://www.bea.gov/.
- Data describing mobility changes, are obtained from https://github.com/descarteslabs/DL-COVID-19 and https://www.safegraph.com/ r
- Gini index, are obtained from American Community Survey data at https://www.census.gov/. The number of ICU beds for each county is obtained from https://www.kaggle.com/jaimeblasco/icu-beds-by-county-in-the-us/data.
Study reliability
This is an observational study; other confounding factors may be responsible for the associations observed.
The R2 of the regression is 30% in China and 12% in the .S., which means about 70% to 88% of cross-city R-value fluctuations cannot be explained by temperature and relative humidity
| Clearly defined setting |
Demographic characteristics described |
Follow-up length was sufficient |
Transmission outcomes assessed |
Main biases are taken into consideration |
|
Unclear |
No |
Yes |
Yes
|
Yes |
What else should I consider?
About the authors
Carl Heneghan
Carl is Professor of EBM & Director of CEBM at the University of Oxford. He is also a GP and tweets @carlheneghan. He has an active interest in discovering the truth behind health research findings
Elizabeth Spencer
Dr Elizabeth Spencer; MMedSci, PhD. Epidemiologist, Nuffield Department for Primary Care Health Sciences, University of Oxford.
