Li Tao, Rong Lili, Zhang Anming
Institute of Systems Engineering, Dalian University of Technology, PR China.
Sauder School of Business, University of British Columbia, Canada.
Transp Policy (Oxf). 2021 Jun;106:226-238. doi: 10.1016/j.tranpol.2021.04.009. Epub 2021 Apr 14.
This paper demonstrates that transportation networks may be used to assess and predict the regional risk of COVID-19 infection from the outbreak. We use China's high-speed rail (HSR) network at the scale of prefecture level to assess, based on a probabilistic risk model, the risk of COVID-19 infection from Wuhan to the country's 31 province-level regions at the early stage of domestic spread. We find that the high-risk regions are mainly distributed along the southern half of Beijing-Hong Kong HSR line, where a large number of infection cases have been confirmed at the early stage. Furthermore, the two components of the infection risk, namely, the probability (proxied by the region's correlation with Wuhan through HSR) and the impact (proxied by the region's population with mobility), can play different roles in the risk ranking for different regions. For public health administrators, these findings may be used for better decision making, including the preparation of emergency plans and supplies, and the allocation of limited resources, before the extensive spread of the epidemic. Moreover, the administrators should adopt different intervention measures for different regions, so as to better mitigate the epidemic spread according to their own risk scenarios with respect to the probability of occurring and, once occurred, the impact.
本文表明，交通网络可用于评估和预测新冠疫情爆发后区域感染风险。我们基于概率风险模型，利用中国地级市层面的高铁网络，评估了国内疫情初期新冠病毒从武汉传播至全国31个省级行政区的感染风险。我们发现，高风险地区主要分布在北京-香港高铁线路南段沿线，该区域在疫情初期确诊了大量感染病例。此外，感染风险的两个组成部分，即概率（通过高铁与武汉的关联程度来衡量）和影响（通过有流动的地区人口来衡量），在不同地区的风险排名中可能发挥不同作用。对于公共卫生管理人员而言，在疫情广泛传播之前，这些发现可用于更好地决策，包括制定应急预案和物资储备，以及分配有限资源。此外，管理人员应针对不同地区采取不同的干预措施，以便根据各地区发生疫情的概率及其一旦发生后的影响所对应的风险情况，更好地减轻疫情传播。
