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Computational methods for vulnerability analysis and resource allocation in public health emergencies.

机译：公共卫生紧急情况下的脆弱性分析和资源分配的计算方法。

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摘要

POD (Point of Dispensing)-based emergency response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable sub-populations, resulting in access disparities during emergency response. Federal authorities emphasize on the need to identify sub-populations that cannot avail regular services during an emergency due to their special needs to ensure effective response. Vulnerable individuals require the targeted allocation of appropriate resources to serve their special needs. Devising schemes to address the needs of vulnerable sub-populations is essential for the effectiveness of response plans. This research focuses on data-driven computational methods to quantify and address vulnerabilities in response plans that require the allocation of targeted resources.;Data-driven methods to identify and quantify vulnerabilities in response plans are developed as part of this research. Addressing vulnerabilities requires the targeted allocation of appropriate resources to PODs. The problem of resource allocation to PODs during public health emergencies is introduced and the variants of the resource allocation problem such as the spatial allocation, spatio-temporal allocation and optimal resource subset variants are formulated. Generating optimal resource allocation and scheduling solutions can be computationally hard problems. The application of metaheuristic techniques to find near-optimal solutions to the resource allocation problem in response plans is investigated. A vulnerability analysis and resource allocation framework that facilitates the demographic analysis of population data in the context of response plans, and the optimal allocation of resources with respect to the analysis are described.
机译：当考虑区域内点胶点的选择，总体人口密度和估计的交通需求时，基于POD（点胶点）的涉及大规模预防的应急响应计划似乎是可行的。但是，该计划可能无法满足特定的弱势群体的需求，从而导致在应急响应期间的出入不均。联邦当局强调必须确定因紧急情况而无法提供常规服务的亚群，以确保有效响应。弱势群体需要有针对性地分配适当的资源来满足他们的特殊需求。制定解决弱势亚人群需求的计划对于响应计划的有效性至关重要。这项研究的重点是数据驱动的计算方法，以量化和解决需要分配目标资源的应急计划中的漏洞;数据驱动的方法来识别和量化应急计划中的漏洞是本研究的一部分。解决漏洞需要针对​​POD有针对性地分配适当的资源。介绍了突发公共卫生事件中POD的资源分配问题，并提出了资源分配问题的变体，例如空间分配，时空分配和最佳资源子集变体。生成最佳资源分配和调度解决方案可能是计算难题。研究了元启发式技术在响应计划中寻找资源分配问题的最优解的应用。描述了一种脆弱性分析和资源分配框架，该框架有助于在响应计划的背景下对人口数据进行人口统计分析，并针对该分析对资源进行了最佳分配。

著录项

  • 作者

    Indrakanti, Saratchandra.;

  • 作者单位

    University of North Texas.;

  • 授予单位 University of North Texas.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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