IUCRC RAPID: Collaborative Research: Rapid Detection & Systems Modeling for Containment and Casualty Mitigation in Ebola Outbreak

IUCCRC RAPID：合作研究：快速检测

• 批准号: 1516207
• 负责人: Deyang Qu
• 金额: $ 9.46万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1516207&HistoricalAwards=false
• 关键词: IUCRC; RAPID; Collaborative; Research; Rapid

Emergency response and medical preparedness are primary missions of the Centers for Disease Control and Prevention (CDC). SARs, bird flu, H1N1, and the recent Ebola crisis in W. Africa underscore the critical importance of preparedness and response. Such needs are wide-spread as globalization and air transportation facilitate rapid disease spread across the world. The on-the-ground response operation in W. Africa is volatile and time-critical. Every policy made, and every resource made available must be done intelligently to facilitate rapid containment and effective treatment of the ill to save lives. This proposal involves advances and development of a real-time decision support system along with a real-time detection sensor that can be used by regional and local public health responders to prepare for and deal with pandemic emergency situations. The work is critical for our national medical preparedness, emergency response and population health security; and is urgent to the current W. African Ebola combat. It allows emergency planners to: (i) determine efficient resource allocation and operations for medical response and rapid detection, accommodating on-the-fly changes as the situation evolves; (ii) monitor within-facility cross contamination and disease propagation and provide guidance on effective protection of caretakers; (iii) train regional public health agents for emergency preparedness and familiarize them with procedural steps for screening, handling patients, medical services, and decontamination; (iv) analyze and assess the adequacy of existing resources (locally and from international aid organizations), and identify budget, labor, and training needs to facilitate rapid containment of Ebola; and to optimize treatment of the ill under resource-stressed environments; (v) estimate costs and resources needed for the protection of the general population, including regions outside W. Africa; and (vi) perform large-scale virtual exercises to prepare public health workers for pandemic scenarios. Computational modeling and technology that facilitate rapid detection of infected individuals, containment strategy development, tradeoff analysis, optimal resource allocation, and look-ahead vision of results are of paramount importance for combating infectious disease outbreaks. Such capabilities are fundamental to the public health emergency response infrastructure, and are critical to our national public health population protection mission. The goal of this project is to support the current mission of World Health Organization, the US Military Operation United Assistance, and CDC in the combat against Ebola. The containment of Ebola in W. Africa is fundamental to preventing a global epidemic. Specifically, two aims will be carried out. First, a computational decision support framework to optimize scarce resources for rapid disease containment will be designed and implemented. Our approach will couple a disease propagation model with both a treatment queuing model and optimization engine to determine the optimal resources needed for disease containment. The resulting system will empower on-the-ground policy makers with strategies for effective casualty mitigation, risk monitoring and tracing, and population protection during a pandemic, under strained time and limited medical/labor supplies. The proposed system has real-time capability for live data-feeds and allows re-configuration on the fly as the event unfolds. It will be tailored for the current Ebola response effort in W. Africa, and for CDC public health preparedness within the United States. High-risk populations identified by our system will be fed into the second aim for rapid and early detection. Specifically, a handheld device that integrates real-time sensors for Ebola virus detection in saliva will be prototyped and tested using the state-of-the-art nanotechnology. Assisted by infectious disease experts, experiments will be carried out to study the technical performance of sensor sensitivity and selectivity and to develop a handheld meter along with a sensor test strip for on-site testing. The resulting nanosensor will be reliable and cost-effective with real-time capability for Ebola virus detection in saliva. The work addresses an urgent need to support the W. African Ebola response and to advance our national emergency preparedness capabilities. The real-time disease-containment resource-optimization decision-support system offers a powerful modeling environment that can be used and tailored for investigating and responding to emergencies involving Ebola and other biological agents, as well as all types of man-made or natural disasters, in resource-stressed environments. The handheld sensor offers easy-to-use cost-effective real-time capability that does not require special training. Optimal resource allocation to minimize disease spread and early detection are crucial elements to successful containment.

紧急响应和医疗准备是疾病控制与预防中心（CDC）的主要任务。 SARS，鸟流感，H1N1和非洲最近的埃博拉病毒危机强调了准备和反应的至关重要。这样的需求广泛扩展，因为全球化和航空运输促进了全世界的快速疾病。 W.非洲的地面响应操作是挥发性和关键时期的。制定的每项政策和所提供的每项资源都必须智能地完成，以促进快速遏制和有效治疗疾病以挽救生命。该提案涉及实时决策支持系统的进步和开发以及一个实时检测传感器，该传感器可以由地区和地方公共卫生响应者使用，以准备并处理大流行的紧急情况。这项工作对于我们的国家医疗准备，应急和人口健康安全至关重要；并且对当前的非洲埃博拉病毒战斗非常迫切。它允许应急计划人员：（i）确定有效的资源分配和医疗响应和快速检测的操作，随着情况的发展，可以随时进行更改； （ii）监测实物内部的交叉污染和疾病传播，并提供有关护理人员有效保护的指导； （iii）培训区域公共卫生特工以进行紧急准备，并熟悉筛查，处理患者，医疗服务和净化的程序步骤； （iv）分析和评估现有资源的充分性（本地和国际援助组织），并确定预算，劳动和培训需要促进埃博拉病毒的快速遏制；并在资源胁迫的环境下优化对病的治疗； （v）估计保护普通人群所需的成本和资源，包括非洲以外的地区； （vi）进行大规模的虚拟练习，为公共卫生工作者准备大流行的情况。促进受感染者快速检测，遏制策略发展，权衡分析，最佳资源分配以及对结果的远见是对打击传染病暴发至关重要的重要性。这种功能对于公共卫生应急响应基础设施至关重要，对于我们国家公共卫生人口保护任务至关重要。该项目的目的是支持世界卫生组织目前的使命，美国军事行动联合援助和CDC在对抗埃博拉病毒中。非洲埃博拉病毒的遏制是防止全球流行病的基础。具体来说，将实现两个目标。首先，将设计和实施一个计算决策支持框架，以优化用于快速疾病遏制的稀缺资源。 我们的方法将使疾病传播模型与治疗排队模型和优化引擎相结合，以确定疾病控制所需的最佳资源。最终的系统将使现场决策者有效地制定有效减轻伤亡，风险监测和追踪以及在大流行期间，在紧张的时间和有限的医疗/劳动力供应下进行的人口保护的策略。所提出的系统具有实时数据馈送的实时功能，并允许随着事件的展开而即时进行重新配置。它将针对非洲W.非洲的当前埃博拉病毒反应工作以及美国境内的CDC公共卫生准备。我们系统确定的高风险人群将被送入快速和早期检测的第二个目标。具体而言，将使用最先进的纳米技术进行原型和测试，将唾液中埃博拉病毒检测的实时传感器集成用于埃博拉病毒检测的手持设备。在传染病专家的协助下，将进行实验，以研究传感器敏感性和选择性的技术性能，并开发手持仪表以及传感器测试带进行现场测试。由此产生的纳米传感器将是可靠且具有成本效益的，具有实时能力，可用于唾液中的埃博拉病毒检测。这项工作迫切需要支持W.非洲埃博拉病毒的反应并提高我们的国家应急能力。实时疾病接触资源优化决策支持系统提供了一个强大的建模环境，可用于调查和响应涉及埃博拉病毒和其他生物学药物的紧急情况，以及在资源压力环境中的所有类型的人造或自然灾害。手持传感器提供易于使用的成本效益的实时功能，不需要特殊培训。最佳资源分配以最大程度地减少疾病的扩散和早期检测是成功遏制的关键因素。