key: cord-0930579-ifkxrnrl
authors: Wei, Jia-Te; Liu, Yun-Xia; Zhu, Yu-Chen; Qian, Jie; Ye, Run-Ze; Li, Chun-Yu; Ji, Xiao-Kang; Li, Hong-Kai; Qi, Chang; Wang, Ying; Yang, Fan; Zhou, Yu-Hao; Yan, Ran; Cui, Xiao-Ming; Liu, Yuan-Li; Jia, Na; Li, Shi-Xue; Li, Xiu-Jun; Xue, Fu-Zhong; Zhao, Lin; Cao, Wu-Chun
title: Impacts of Transportation and Meteorological Factors on the Transmission of COVID-19
date: 2020-08-26
journal: Int J Hyg Environ Health
DOI: 10.1016/j.ijheh.2020.113610
sha: f79b58aa85ce04221aadefe178fbc1f3f6358c75
doc_id: 930579
cord_uid: ifkxrnrl

The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9,750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p =0.001), 2.07 (p <0.001), 1.31 (p =0.04), and 1.70 (p <0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.

The outbreak process of COVID-19 in mainland China mainly occurred in February, 67 and has been well-controlled from March to now with very low incidence, most of 68 which are imported abroad. 69 Before the lockdown, more than five million people have already left Wuhan by 70 train, bus or plane for Spring Festival holidays, which might lead to rapid spread 92 We collected data of confirmed COVID-19 cases from the National Notifiable (Table 1) . 175 To identify the effects of meteorological variables on the local transmission, we Table S1 (Table S1 ). Final model included 179 average temperature, cumulative precipitation and average wind speed, after 180 adjustment for distance to Wuhan and population density (Table S2 ). The results of 181 GAM revealed that the relationships between meteorological factors and COVID-19 182 were nonlinear. The risk of COVID-19 decreased with the increasing average 183 temperature when temperature was relatively low (Fig. 2a) . When cumulative 184 precipitation was less than around 50 mm, increasing cumulative precipitation was 185 correlated with higher risk of COVID-19. When cumulative precipitation was over 50 186 mm, the risk of COVID-19 decreased with increasing cumulative precipitation but the 187 J o u r n a l P r e -p r o o f confidence interval is much wider (Fig. 2b ). The wind speed had a positive effect on 188 the risk of COVID-19 when the speed was lower than 1.5 m/s or higher than 2.5 m/s, 189 but had a negative effect between 1.5 m/s and 2.5 m/s (Fig. 2c) . and these eases of restriction did not cause the epidemic rebound until now (Fig. 3) . 

Do Weather Temperature and Median-age affect COVID-19