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

应急响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 流感以及最近发生在西非的埃博拉危机都凸显了做好准备和应对的重要性。随着全球化和航空运输促进疾病在世界范围内迅速传播，这种需求广泛存在。西非的实地响应行动不稳定且时间紧迫。制定的每项政策和提供的每一项资源都必须明智地实施，以促进快速遏制和有效治疗疾病以拯救生命。该提案涉及实时决策支持系统以及实时检测传感器的进步和开发，区域和地方公共卫生响应人员可以使用该系统来准备和应对大流行的紧急情况。这项工作对于我们国家的医疗准备、应急响应和人口健康安全至关重要；对当前西非埃博拉疫情的防治具有紧迫性。它使应急计划人员能够：(i) 确定医疗响应和快速检测的有效资源分配和操作，适应形势发展的动态变化； (ii) 监测设施内的交叉污染和疾病传播，并为有效保护看护人员提供指导； (iii) 对地区公共卫生人员进行应急准备培训，并使他们熟悉筛查、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（当地和国际援助组织）是否充足，并确定预算、劳动力和培训需求，以促进快速遏制埃博拉病毒；并在资源紧张的环境下优化对病人的治疗； (v) 估计保护普通民众（包括西非以外地区）所需的成本和资源； (vi) 进行大规模虚拟演习，让公共卫生工作者为应对大流行情况做好准备。有助于快速检测感染者、制定遏制策略、权衡分析、优化资源分配和前瞻性结果的计算模型和技术对于抗击传染病爆发至关重要。这些能力是公共卫生应急响应基础设施的基础，也是我们国家公共卫生人口保护使命的关键。该项目的目标是支持世界卫生组织、美国军事行动联合援助和疾病预防控制中心当前抗击埃博拉病毒的使命。西非埃博拉病毒的遏制对于防止全球流行病至关重要。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，以优化稀缺资源以快速遏制疾病。 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决策者能够制定在大流行期间、在时间紧张和医疗/劳动力供应有限的情况下有效减轻伤亡、风险监测和追踪以及人口保护的战略。所提出的系统具有实时数据馈送的能力，并允许在事件发生时动态重新配置。它将针对西非目前的埃博拉应对工作以及美国疾病预防控制中心的公共卫生准备工作进行量身定制。我们的系统识别出的高危人群将被纳入第二个目标，以实现快速、早期检测。具体来说，将使用最先进的纳米技术对集成实时传感器的手持设备进行原型设计并测试唾液中的埃博拉病毒。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，并开发手持式仪表和用于现场测试的传感器测试条。由此产生的纳米传感器将可靠且具有成本效益，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工作和提高我们国家应急准备能力的迫切需要。实时疾病遏制资源优化决策支持系统提供了强大的建模环境，可用于调查和应对资源紧张环境中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易于使用、经济高效的实时功能，无需特殊培训。优化资源分配以最大程度地减少疾病传播和早期发现是成功遏制疾病的关键要素。

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.