Background
Effect of meteorological factors such as air temperature, humidity, and sunlight exposure on transmission dynamics of novel coronavirus disease 2019 (COVID-19) remains controversial. We investigated the association of these factors on COVID-19 incidence in Japan.
Methods
We analyzed data on reverse transcription polymerase chain reaction confirmed COVID-19 cases for each prefecture (total=47) in Japan and incidence rate was defined as the number of all reported cumulative cases from January 15 to March 17, 2020. Independent variables of each prefecture included three climatic variables (mean values of air temperature, relative humidity, and sunlight exposure), population elderly ratio, and the number of inbound travelers from China during February 2020. Multivariable-adjusted Poisson regression model was constructed to estimate COVID-19 incidence rate ratio (IRR) of independent variables.
Results
There was a total of 702 cases during the study period in Japan (population=125, 900,000). Mean±standard deviation values of meteorological variables were 7.12°C±2.91°C for air temperature, 67.49%±7.63% for relative humidity, and 46.77±12.55% for sunlight exposure. Poisson regression model adjusted for climate variables showed significant association between the incidence and three climatic variables: IRR for air temperature 0.854 (95% confidence interval [CI], 0.804–0.907; P<0.0001), relative humidity 0.904 (95% CI, 0.864–0.945; P<0.0001), and sunlight exposure 0.973 (95% CI, 0.951–0.997; P=0.026).
Conclusion
Higher values of air temperature, relative humidity and sunlight exposure were associated with lower incidence of COVID-19. Public health interventions against COVID-19 epidemic in a country should be developed by considering these meteorological factors.

Citations

• Effect of Long-Distance Domestic Travel Ban Policies in Japan on COVID-19 Outbreak Dynamics During Dominance of the Ancestral Strain: Ex Post Facto Retrospective Observation Study
  Junko Kurita, Yoshitaro Iwasaki
  Online Journal of Public Health Informatics.2024; 16: e44931.     CrossRef
• Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface
  Igor Nesteruk
  Infectious Disease Modelling.2023; 8(3): 806.     CrossRef