tetano
Editor, Senior Moderator
Int J Hyg Environ Health
. 2020 Aug 26;230:113610.
doi: 10.1016/j.ijheh.2020.113610. Online ahead of print.
Impacts of transportation and meteorological factors on the transmission of COVID-19
Jia-Te Wei[SUP] 1 [/SUP], Yun-Xia Liu[SUP] 2 [/SUP], Yu-Chen Zhu[SUP] 2 [/SUP], Jie Qian[SUP] 3 [/SUP], Run-Ze Ye[SUP] 1 [/SUP], Chun-Yu Li[SUP] 2 [/SUP], Xiao-Kang Ji[SUP] 2 [/SUP], Hong-Kai Li[SUP] 2 [/SUP], Chang Qi[SUP] 2 [/SUP], Ying Wang[SUP] 2 [/SUP], Fan Yang[SUP] 2 [/SUP], Yu-Hao Zhou[SUP] 4 [/SUP], Ran Yan[SUP] 2 [/SUP], Xiao-Ming Cui[SUP] 4 [/SUP], Yuan-Li Liu[SUP] 3 [/SUP], Na Jia[SUP] 4 [/SUP], Shi-Xue Li[SUP] 5 [/SUP], Xiu-Jun Li[SUP] 5 [/SUP], Fu-Zhong Xue[SUP] 6 [/SUP], Lin Zhao[SUP] 7 [/SUP], Wu-Chun Cao[SUP] 8 [/SUP], Cheeloo EcoHealth Consortium (CLEC)
Affiliations
Abstract
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.
Keywords: COVID-19; China; Interaction; Meteorological factors; Transportation.
. 2020 Aug 26;230:113610.
doi: 10.1016/j.ijheh.2020.113610. Online ahead of print.
Impacts of transportation and meteorological factors on the transmission of COVID-19
Jia-Te Wei[SUP] 1 [/SUP], Yun-Xia Liu[SUP] 2 [/SUP], Yu-Chen Zhu[SUP] 2 [/SUP], Jie Qian[SUP] 3 [/SUP], Run-Ze Ye[SUP] 1 [/SUP], Chun-Yu Li[SUP] 2 [/SUP], Xiao-Kang Ji[SUP] 2 [/SUP], Hong-Kai Li[SUP] 2 [/SUP], Chang Qi[SUP] 2 [/SUP], Ying Wang[SUP] 2 [/SUP], Fan Yang[SUP] 2 [/SUP], Yu-Hao Zhou[SUP] 4 [/SUP], Ran Yan[SUP] 2 [/SUP], Xiao-Ming Cui[SUP] 4 [/SUP], Yuan-Li Liu[SUP] 3 [/SUP], Na Jia[SUP] 4 [/SUP], Shi-Xue Li[SUP] 5 [/SUP], Xiu-Jun Li[SUP] 5 [/SUP], Fu-Zhong Xue[SUP] 6 [/SUP], Lin Zhao[SUP] 7 [/SUP], Wu-Chun Cao[SUP] 8 [/SUP], Cheeloo EcoHealth Consortium (CLEC)
Affiliations
- PMID: 32896785
- DOI: 10.1016/j.ijheh.2020.113610
Abstract
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.
Keywords: COVID-19; China; Interaction; Meteorological factors; Transportation.