RAPID: Computational Methods for Quantifying Regional Ebola-Specific Resource Coverage

联邦、州和地方机构必须了解针对埃博拉病毒的地区性资源的可用性，以制定有效的应对计划。这项研究将导致制定方法，将与现有区域卫生基础设施有关的数据与特定人群的信息结合起来，以确定减轻潜在埃博拉疫情的能力和资源。主要重点是设计和实施计算工具，以促进量化资源在整个区域医疗基础设施中的可用性和分布，特别是与埃博拉患者隔离和治疗有关的资源。这项研究将产生更广泛的影响，改进分配有限卫生资源的最佳做法，以减轻新出现或重新出现的传染病的影响，这些疾病可能威胁到美国。由此产生的计算框架可能成为国家化学、生物、辐射或核(CBRN)准备工作的一个组成部分。此外，拟议的研究将涉及学生，为他们提供在跨学科STEM项目中进行研究的机会。拟议的计算方法将构建区域医疗机构及其相应的埃博拉特有能力的层次结构。诸如Voronoi细分和均匀划分(UPAS)等地理空间划分算法应构成描述与可用医疗设施对应的服务区域的基础，从而量化人均资源可获得性。这将使各机构能够解决现有的资源限制，并制定有效分配针对埃博拉病毒的资源的战略，以实现适当的区域复盖。预计资源限制将对不同暴发程度的缓解工作产生不同的影响。随着疫情规模的增加，必须提供足够的地方和区域资源，以反映增加的需求。拟议的计算方法应提供必要的功能，以估计不同暴发规模所需的资源可获得性水平。为加强现有规划框架而设计和实施的新方法(RE-PLAN)将把其适用范围扩大到与缓解埃博拉有关的卫生资源的地理空间分配。

Federal, state, and local agencies must understand the availability of regional Ebola-specific resources to develop effective response plans. This research shall result in the development of methodologies to integrate data pertaining to available regional health infrastructure with population-specific information to identify capabilities and resources for mitigating a potential Ebola outbreak. The primary focus is the design and implementation of computational tools that facilitate the quantification of the availability and distribution of resources across regional healthcare infrastructure, specifically resources that pertain to the isolation and treatment of Ebola patients. This research will have broader impact by improving best practices for the allocation of limited health resources to mitigate the effects of emerging or reemerging infectious diseases, which may threaten the United States. The resulting computational framework may become an integral component of the national chemical, biological, radiological, or nuclear (CBRN) preparedness effort. Further, the proposed research will involve students, providing them an opportunity to conduct research in an interdisciplinary STEM project.The proposed computational methods will construct a hierarchy of regional healthcare facilities and their corresponding Ebola-specific capabilities. Geospatial partitioning algorithms, such as Voronoi tessellation and uniform partitioning (UPAS) shall form the basis for delineating service regions corresponding to available healthcare facilities, thereby quantifying the per capita resource availability. This will enable agencies to address existing resource constraints and develop strategies for the effective allocation of Ebola-specific resources to achieve adequate regional coverage. Resource constraints can be expected to affect mitigation efforts differently at different levels of outbreak magnitude. As the magnitude of the outbreak increases, adequate local and regional resources must be made available to reflect the increased demand. The proposed computational methods shall provide the necessary functionalities to estimate the required levels of resource availability for different levels of outbreak magnitude. The design and implementation of new methods to augment an existing planning framework (RE-PLAN) will extend its applicability to the geospatial allocation of health resources pertinent to Ebola mitigation.