Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations

Abstract

Background: Meteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.

Methods: In this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (R_eff ). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.

Results: Precipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.

Conclusion: Meteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.

Keywords: COVID-19; generalized estimating equations; lagged effect; meteorological factors; transmissibility.

Figure 1

Change in trend of key meteorological factors in 30 provinces and cities. (A) daily average temperature; (B) relative humidity; (C) precipitation; (D) sunshine hours; (E) air pressure; (F) wind speed.

Figure 2

Trend of transmissibility, incidence rate, and the number of reported cases. (A) transmissibility; (B) incidence rate; (C) the number of reported cases.

Figure 3

Analysis results of influencing factors based on the generalized estimation equation. Model 1: the just-in-time effect model; model 2: 1 day lag effect model; model 3: 2 days lag effect model; model 4: 3 days lag effect model; model 5: 4 days lag effect model; model 6: 5 days lag effect model; p is based on the results of the generalized estimating equation; *** = p < 0.001; ** = p < 0.01; * = p < 0.1. All correlation coefficients |r| were <0.8, so there was no strong correlation between x1 and x6; the variance inflation factor (VIF) values among all covariates were between 1.21 and 3.58, so the collinearity between x1 and x6 in models 1–6 was not substantial.