Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States

We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. This paper was previously circulated under the title “High Temperature and High Humidity Reduce the Transmission of COVID-19”.