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

应急反应和医疗准备是疾病控制和预防中心（CDC）的主要任务。 SARS、禽流感、H1N1和最近在W.非洲强调备灾和救灾的极端重要性。由于全球化和航空运输促进了疾病在全世界的迅速传播，这种需求是广泛的。在W.非洲局势动荡，时间紧迫。制定的每一项政策和提供的每一项资源都必须明智地使用，以促进迅速遏制和有效治疗病人，以挽救生命。这项建议涉及到一个实时决策支持系统的进展和发展，该系统沿着一个实时检测传感器，可供区域和地方公共卫生应急人员用于准备和处理大流行病紧急情况。这项工作对我们国家的医疗准备、应急反应和人口健康安全至关重要，对当前的W。非洲埃博拉疫情它使应急规划人员能够：㈠确定医疗反应和快速检测的有效资源分配和业务，适应随着局势发展的动态变化; ㈡监测设施内交叉污染和疾病传播，并就有效保护护理人员提供指导; ㈢培训区域公共卫生人员做好应急准备，并使他们熟悉筛查、处理病人、医疗服务和消除污染的程序步骤;（iv）分析和评估现有资源的充足性（本地和来自国际援助组织的资源），并确定预算、劳动力和培训需求，以促进快速遏制埃博拉;并优化资源紧张环境下的病人治疗;（v）估计保护普通民众所需的成本和资源，包括西撒哈拉以外的地区。（vi）进行大规模虚拟演习，使公共卫生工作者为大流行情景做好准备。促进快速检测受感染个体、遏制策略制定、权衡分析、最佳资源分配和结果前瞻性愿景的计算建模和技术对于抗击传染病爆发至关重要。这种能力对于公共卫生应急基础设施至关重要，对于我们国家的公共卫生人口保护使命至关重要。该项目的目标是支持世界卫生组织、美国联合援助军事行动和疾病预防控制中心在抗击埃博拉方面的当前使命。在W.非洲是预防全球流行病的根本。具体而言，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，以优化快速遏制疾病的稀缺资源。 我们的方法将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。由此产生的系统将使当地的政策制定者能够在时间紧张和医疗/劳动力供应有限的情况下，在大流行病期间有效减轻伤亡，风险监测和追踪以及人口保护的战略。拟议的系统具有实时数据反馈的能力，并允许在活动展开时进行重新配置。它将针对W.非洲，以及美国疾病预防控制中心的公共卫生准备。我们的系统确定的高风险人群将被输入第二个目标，以便快速和早期发现。具体来说，将使用最先进的纳米技术对一种集成了用于唾液中埃博拉病毒检测的实时传感器的手持设备进行原型制作和测试。在传染病专家的协助下，将开展实验，研究传感器灵敏度和选择性的技术性能，并沿着开发一款手持仪，配合传感器试纸进行现场检测。由此产生的纳米传感器将是可靠和具有成本效益的，具有实时检测唾液中埃博拉病毒的能力。这项工作解决了迫切需要支持W。非洲埃博拉应对和提高我们的国家应急准备能力。实时疾病遏制资源优化决策支持系统提供了一个强大的建模环境，可用于在资源紧张的环境中调查和应对涉及埃博拉和其他生物制剂的紧急情况，以及所有类型的人为或自然灾害。手持式传感器提供了易于使用的成本效益的实时能力，不需要特殊的培训。最佳资源分配以尽量减少疾病传播和早期发现是成功遏制的关键因素。

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.