A new epidemics-logistics model: Insights into controlling the Ebola virus disease in West Africa
A new epidemics-logistics model: Insights into controlling the Ebola virus disease in West Africa
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DOI：
10.1016/j.ejor.2017.08.037
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发表时间：
2018-03-16
影响因子：
6.4
通讯作者：
Kibis, Eyyub Y.
中科院分区：
文献类型：
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作者：
Buyuktahtakm, I. Esra;des-Bordes, Emmanuel;Kibis, Eyyub Y.
分区模型一直是研究流行病的一种现象。然而，现有的分区模型没有明确地同时考虑流行病的空间传播和物流问题。在这项研究中，我们通过引入一种新的流行病-物流混合整数规划(MIP)模型来解决这一局限性，该模型确定了用于控制传染病爆发的资源的最佳数量、时间和位置，同时考虑了其空间传播动力学。这一拟议模型的目标是在多时期规划范围内，在有限的预算下最大限度地减少感染和死亡的总人数。本研究是第一个空间显式优化方法，它考虑了疾病传播率的地理差异、感染者在不同地区的迁移以及由于治疗中心能力有限而导致的不同治疗率。我们以2014-2015年几内亚、利比里亚和塞拉利昂的埃博拉疫情为例，说明了MIP模型的性能。我们的结果为这些受影响最严重的国家的每个特定区域提供了干预时间和强度的明确信息。我们的模型预测与真实的暴发数据非常吻合，并表明在治疗和隔离方面的大量前期投资可以最有效地利用资源，将感染降至最低。所提出的建模框架可用于研究其他传染病，并为控制大规模时空尺度上的传染病暴发提供切实的政策建议。(C)2017爱思唯尔B.V.保留所有权利。
Compartmental models have been a phenomenon of studying epidemics. However, existing compartmental models do not explicitly consider the spatial spread of an epidemic and logistics issues simultaneously. In this study, we address this limitation by introducing a new epidemics-logistics mixed-integer programming (MIP) model that determines the optimal amount, timing and location of resources that are allocated for controlling an infectious disease outbreak while accounting for its spatial spread dynamics. The objective of this proposed model is to minimize the total number of infections and fatalities under a limited budget over a multi-period planning horizon. The present study is the first spatially explicit optimization approach that considers geographically varying rates for disease transmission, migration of infected individuals over different regions, and varying treatment rates due to the limited capacity of treatment centers. We illustrate the performance of the MIP model using the case of the 2014-2015 Ebola outbreak in Guinea, Liberia, and Sierra Leone. Our results provide explicit information on intervention timing and intensity for each specific region of these most affected countries. Our model predictions closely fit the real outbreak data and suggest that large upfront investments in treatment and isolation result in the most efficient use of resources to minimize infections. The proposed modeling framework can be adopted to study other infectious diseases and provide tangible policy recommendations for controlling an infectious disease outbreak over large spatial and temporal scales. (C) 2017 Elsevier B.V. All rights reserved.