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
Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China.
Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China.
Int J Hyg Environ Health. 2020 Sep;230:113610. doi: 10.1016/j.ijheh.2020.113610. Epub 2020 Aug 26.
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 9750.4 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.
2019年新型冠状病毒病(COVID-19)的持续大流行正对全球公共卫生应对系统构成挑战。我们旨在利用中国大陆的数据确定COVID-19传播的风险因素。我们估算了县级别的罹患率(AR)。采用逻辑回归来探讨交通在全国传播中的作用。构建广义相加模型和分层线性混合效应模型以确定多种气象因素对本地传播的影响。受影响县的罹患率为每百万人0.6至9750.4,中位数为8.8。与铁路、高速公路、国道相交或有机场的县,COVID-19风险显著更高,调整后的比值比(OR)分别为1.40(p = 0.001)、2.07(p < 0.001)、1.31(p = 0.04)和1.70(p < 0.001)。COVID-19较高的罹患率与较低的平均温度、适度的累积降水量和较高风速显著相关。在上述三种气象因素之间发现了显著的两两交互作用,在低温和适度降水情况下COVID-19风险更高。随着风速增加,温暖地区该疾病风险也可能更高。总之,交通和气象因素可能在中国大陆COVID-19传播中发挥重要作用,公共卫生警报系统可综合考虑这些因素以更好地预防该疾病。