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

应急和医疗准备是疾病控制和预防中心(CDC)的主要任务。非典、禽流感、甲型H1N1流感以及最近在西非爆发的埃博拉危机凸显了防备和应对的极端重要性。随着全球化和航空运输促进了疾病在世界各地的快速传播，这种需求得到了广泛的传播。西非的实地反应行动是不稳定的，而且时间紧迫。制定的每一项政策和提供的每一项资源都必须明智地进行，以促进对疾病的快速遏制和有效治疗，以拯救生命。这项建议涉及推进和开发实时决策支持系统以及实时检测传感器，区域和地方公共卫生响应人员可以使用该传感器来准备和处理大流行紧急情况。这项工作对我们国家的医疗准备、应急响应和人口健康安全至关重要；对当前西非抗击埃博拉病毒具有紧迫性。它使应急规划者能够：(1)为医疗反应和快速检测确定有效的资源分配和操作，以适应情况发展的即时变化；(2)监测设施内的交叉污染和疾病传播，并就有效保护照顾者提供指导；(3)培训区域公共卫生人员做好应急准备，并使他们熟悉筛查、处理患者、医疗服务和净化的程序步骤；(4)分析和评估现有资源(当地和国际援助组织)的充分性，并确定预算、劳动力和培训需求，以促进迅速遏制埃博拉病毒；并在资源紧张的环境下优化对病人的治疗；(V)估计保护包括西非以外地区在内的普通民众所需的费用和资源；(Vi)开展大规模虚拟演习，使公共卫生工作者做好应对大流行情况的准备。有助于快速检测感染者、制定遏制策略、权衡分析、优化资源分配和对结果进行前瞻性展望的计算模型和技术，对于抗击传染病暴发至关重要。这些能力是公共卫生应急基础设施的基础，对我们的国家公共卫生人口保护任务至关重要。该项目的目标是支持世界卫生组织、美军联合援助行动和疾控中心目前的使命，抗击埃博拉病毒。在西非遏制埃博拉疫情是防止全球疫情的根本。具体地说，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，以优化用于快速控制疾病的稀缺资源。我们的方法将疾病传播模型与治疗排队模型和优化引擎结合起来，以确定疾病遏制所需的最佳资源。由此产生的系统将使实地政策制定者能够在紧张的时间和有限的医疗/劳动力供应下，在大流行期间有效减少伤亡、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馈送的能力，并允许在事件展开时动态重新配置。它将为西非目前的埃博拉应对工作以及美国疾控中心的公共卫生准备工作量身定做。我们的系统确定的高危人群将被纳入第二个目标，以实现快速和早期检测。具体地说，一种集成了用于检测唾液中埃博拉病毒的实时传感器的手持设备将使用最先进的纳米技术进行原型和测试。在传染病专家的协助下，将进行实验，研究传感器灵敏度和选择性的技术性能，并开发一种手持仪表以及用于现场测试的传感器测试条。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。这项工作解决了支持西非埃博拉应对和提高我们国家应急准备能力的迫切需要。实时疾病控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。该手持式传感器提供简单易用、性价比高的实时功能，不需要特殊培训。优化资源分配以最大限度地减少疾病传播和及早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.