论文标题
具有不确定资源的数据驱动传染病控制
Data-Driven Infectious Disease Control with Uncertain Resources
论文作者
论文摘要
我们研究了一种资源分配问题，该问题用于在种群中含有传染病，但受资源不确定性的影响。我们提出了一个两阶段模型，决策者试图在第二阶段资源是随机的两个阶段分配资源。我们使用数据驱动的功能形式来对受感染个体的累积数进行建模，而不是在优化模型的约束中控制流行轨迹的非线性微分方程系统。这种灵活的数据驱动的建模选择使我们能够将优化问题转换为可处理的混合整数线性程序。我们的灵活方法可以处理在线决策过程，在此过程中，决策者使用有关流行病进展的新信息更新了开放治疗单元的决策和分配床。我们利用了一个详细的仿真模型，该模型通过塞拉利昂2014年埃博拉病毒流行的真实数据验证。我们的结果表明，与实际流行期间所采用的政策相比，我们的政策在塞拉利昂的受感染者数量少约400。我们还提供了通过优化框架生成的分配策略的详细比较，该策略阐明了不同地区的最佳资源分配。
We study a resource allocation problem for containing an infectious disease in a metapopulation subject to resource uncertainty. We propose a two-stage model where the policy maker seeks to allocate resources in both stages where the second stage resource is random. Instead of a system of nonlinear differential equations that governs the epidemic trajectories in the constraints of the optimization model, we use a data-driven functional form to model the cumulative number of infected individuals. This flexible data-driven modeling choice allows us to transform the optimization problem to a tractable mixed integer linear program. Our flexible approach can handle an online decision making process, where the decision makers update their decisions for opening treatment units and allocating beds utilizing the new information about the epidemic progress. We utilize a detailed simulation model, validated by real data from the 2014 Ebola epidemic in Sierra Leone. Our results show that our policies produce about 400 fewer number of infected individuals in Sierra Leone compared to the policies applied during the actual epidemic. We also provide a detailed comparison of allocation policies generated by our optimization framework which sheds light on the optimal resource allocation in different regions.
