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.

COVID-19 的出现明智地提醒我们社会对流行病的脆弱性。我们需要为寻找阻止此类疫情爆发的方法做好更好的准备。除了保持社交距离和戴口罩外，限制人员流动是控制传播的一项重要措施。此类关于锁定/减少运动的决定应以知情的方式做出，并因此建模为优化问题。我们提出了早期运输封锁和检疫问题（TLQP），它可以帮助决定一个国家的交通基础设施的哪些部分应该在早期阶段受到限制。在基于网络的易感暴露感染恢复（SEIR）模型之上，我们建立了一个决策推荐框架，该框架考虑了跨境流量的锁定，内部交通和个体人口内部的流动。组合优化问题旨在找到使锁定的社会成本最小化的最佳行动集。鉴于该问题固有的难处理性，我们开发了一种基于有效距离 (ED) 路径和成本效益延迟前向 (CELF) 算法的高效启发式算法。我们对 COVID-19 的全球传播进行并报告实验，并展示各个国家如何通过采取适当措施应对威胁性大流行来保护其人口。我们相信，我们的研究有助于制定应对当前和未来流行病爆发的措施。组合优化问题旨在找到使锁定的社会成本最小化的最佳行动集。鉴于该问题固有的难处理性，我们开发了一种基于有效距离 (ED) 路径和成本效益延迟前向 (CELF) 算法的高效启发式算法。我们对 COVID-19 的全球传播进行并报告实验，并展示各个国家如何通过采取适当措施应对威胁性大流行来保护其人口。我们相信，我们的研究有助于制定应对当前和未来流行病爆发的措施。组合优化问题旨在找到使锁定的社会成本最小化的最佳行动集。鉴于该问题固有的难处理性，我们开发了一种基于有效距离 (ED) 路径和成本效益延迟前向 (CELF) 算法的高效启发式算法。我们对 COVID-19 的全球传播进行并报告实验，并展示各个国家如何通过采取适当措施应对威胁性大流行来保护其人口。我们相信，我们的研究有助于制定应对当前和未来流行病爆发的措施。我们对 COVID-19 的全球传播进行并报告实验，并展示各个国家如何通过采取适当措施应对威胁性大流行来保护其人口。我们相信，我们的研究有助于制定应对当前和未来流行病爆发的措施。我们对 COVID-19 的全球传播进行并报告实验，并展示各个国家如何通过采取适当措施应对威胁性大流行来保护其人口。我们相信，我们的研究有助于制定应对当前和未来流行病爆发的措施。