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

联邦、州和地方机构必须了解区域埃博拉专用资源的可用性，以制定有效的应对计划。这项研究将导致制定方法，将现有区域卫生基础设施的相关数据与特定人群的信息相结合，以确定缓解潜在埃博拉疫情的能力和资源。主要重点是计算工具的设计和实施，这些工具有助于量化区域医疗保健基础设施中资源的可用性和分布，特别是与埃博拉患者隔离和治疗有关的资源。这项研究将通过改善分配有限卫生资源的最佳做法，减轻可能威胁美国的新出现或重新出现的传染病的影响，产生更广泛的影响。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，为他们提供一个机会，在一个跨学科的STEM项目进行研究。拟议的计算方法将构建一个层次的区域医疗保健设施及其相应的埃博拉特定的能力。地理空间分区算法，如Voronoi曲面细分和均匀分区（UPAS），应形成用于描绘与可用医疗保健设施相对应的服务区域的基础，从而量化人均资源可用性。这将使各机构能够解决现有的资源限制问题，并制定有效分配埃博拉专用资源的战略，以实现充分的区域覆盖。在不同的爆发程度上，资源限制预计会对缓解努力产生不同的影响。随着疫情规模的扩大，必须提供足够的地方和区域资源，以反映需求的增加。拟议的计算方法应提供必要的功能，以估计不同爆发规模所需的可用资源水平。设计和实施新的方法，以加强现有的规划框架（RE-ESTA）将扩大其适用性的地理空间分配的卫生资源有关的埃博拉缓解。

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.