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

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

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.