Ding Yida, Wandelt Sebastian, Sun Xiaoqian
School of General Engineering, Beihang University, 100191 Beijing, China.
School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China.
Transp Res Part C Emerg Technol. 2021 Aug;129:103218. doi: 10.1016/j.trc.2021.103218. Epub 2021 May 30.
The advent of COVID-19 is a sensible reminder of the vulnerability of our society to pandemics. We need to be better prepared for finding ways to stem such outbreaks. Except from social distancing and wearing face masks, restricting the movement of people is one important measure necessary to control the spread. Such decisions on the lock-down/reduction of movement should be made in an informed way and, accordingly, modeled as an optimization problem. We propose the Early-stage Transportation Lock-down and Quarantine Problem (TLQP), which can help to decide which parts of the transportation infrastructure of a country should be restricted in early stages. On top of the network-based Susceptible-Exposed-Infectious-Recovered (SEIR) model, we establish a decision recommendation framework, which considers the lock-down of cross-border traffic, internal traffic, and movement inside individual populations. The combinatorial optimization problem aims to find the best set of actions which minimize the social cost of a lock-down. Given the inherent intractability of this problem, we develop a highly-efficient heuristic based on the Effective Distance (ED) path and the Cost-Effective Lazy Forward (CELF) algorithm. We perform and report experiments on the global spread of COVID-19 and show how individual countries may protect their population by taking appropriate measures against the threatening pandemic. We believe that our study contributes to the orchestration of measures for dealing with current and future epidemic outbreaks.
新冠疫情的出现切实提醒我们，我们的社会在面对大流行病时是多么脆弱。我们需要做好更充分的准备，找到遏制此类疫情爆发的方法。除了保持社交距离和佩戴口罩外，限制人员流动是控制疫情传播所需的一项重要措施。关于封锁/减少流动的此类决策应以明智的方式做出，并相应地建模为一个优化问题。我们提出了早期交通封锁与检疫问题（TLQP），它有助于决定一个国家交通基础设施的哪些部分应在早期阶段受到限制。在基于网络的易感-暴露-感染-康复（SEIR）模型之上，我们建立了一个决策建议框架，该框架考虑了跨境交通、国内交通以及个体人群内部的流动封锁。这个组合优化问题旨在找到一组最佳行动，以使封锁的社会成本最小化。鉴于此问题固有的难解性，我们基于有效距离（ED）路径和性价比高的懒惰向前（CELF）算法开发了一种高效启发式算法。我们针对新冠疫情的全球传播进行并报告了实验，展示了各个国家如何通过采取适当措施应对这一威胁性的大流行病来保护其民众。我们相信我们的研究有助于协调应对当前及未来疫情爆发的措施。
