Association of Temperature and Humidity with Transmission of COVID-19 in China

Association of  Temperature and Humidity with Transmission of COVID–19 in China.
Spencer EA, Heneghan  C, Jefferson T.

Published on June 15, 2020

Reference Qi H, Xiao S, Shi R et al Covid-19 transmission in Mainland China is associated with temperature and humidity: a timeseries analysis. Sci Total Environ. 2020;728:138778. 2020
Study type
Country China
Setting 30 provinces
Funding Details Not reported
Transmission mode Meteorological
Exposures Daily average temperature, relative humidity

Bottom Line

Increases in temperature and humidity were associated with lower levels of  COVID-19.

Evidence Summary

Cumulative confirmed cases varied from one (in Tibet) to 33,453 (Hubei), and three-quarters of all confirmed cases occurred in Hubei during the relevant time periods. The daily counts in Hubei rose sharply after the 20th of January. Daily average temperature and relative humidity were negatively associated with COVID-19 with a significant interaction between them in Hubei.

  • Every 1°C increase in the daily average temperature led to a decrease in the daily confirmed cases by 36% to 57% when relative humidity was in the range of 67% to 86%.
  • Every 1% increase in relative humidity led to a decrease in the daily confirmed cases by 11% to 22% daily average temperature was in the range from 5.0°C to 8.2°C.
Graph showing temperature and relative humidity

Every 1% increase in relative humidity led to a decrease in the daily confirmed cases by 11% to 22% daily average temperature was in the range from 5.0°C to 8.2°C.

What did they do?

Cases were obtained from the National Health Commission of China for the following time periods: 1st of December 2019, to 11th of February 2020 in Hubei province; 20th of January 2020 to 11th February 2020 across other provinces. Meteorological data were obtained from An indicator of health-seeking behaviour was assessed by the number of internet searches on the most popular Chinese search engine Baidu index, using terms related to Wuhan pneumonia, coronavirus, etc.

A generalized additive model was fitted to quantify the province-specific associations between meteorological variables and the daily cases of COVID-19 during the study periods.

In the model, the 14-day exponential moving averages of temperature and humidity and their interaction were included and adjusted for time trend and number of coronavirus-related health information internet searches.

Study reliability

The associations were not consistent throughout mainland China. The study period – and time series was longer for Hubei province than other provinces. COVID-19 incidence outside Hubei was more likely due to interventions imposed on the provinces. The meteorological data were collected for the capital city for each province only, which is a common problem in many of these type of studies and requires more accurate estimates.

Clearly defined setting Demographic characteristics described Follow-up length was sufficient Transmission outcomes assessed Main biases are taken into consideration
Unclear No Yes Yes Unclear

What else should I consider?

This is a study from early in the pandemic. Whether its findings apply to later stages and other contexts need considering. The study was unable to consider other potential confounders such as socioeconomic status and did not look at other important environmental exposures such as pollution. The authors discuss the mechanism of the interaction between ambient temperature and humidity. And although the mechanism is unclear they cite (Zhou 2004) that hypothesise that a combination of low temperature and humidity make the nasal mucosa prone to small ruptures, that create an opportunity for the virus to infect invasion.

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.

Tom Jefferson

Tom Jefferson, epidemiologist.