Optimization and decision strategies for medical preparedness and emergency response

Optimization;Disease propagation;Public health emergency response

Chen, Chien-Hung ; Lee, Eva K. Industrial and Systems Engineering Goldberg, David Mei, Yajun Monteiro, Renato D. C. Zha, Hongyuan Pietz, Ferdinand ; Lee, Eva K.

University:Georgia Institute of Technology

Department:Industrial and Systems Engineering

关键词: Optimization;    Disease propagation;    Public health emergency response;   

Others  :  https://smartech.gatech.edu/bitstream/1853/52939/1/CHEN-DISSERTATION-2013.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The public health emergencies, such as bioterrorist attacks or pandemic outbreaks, have gained serious public and government attentions since the 2001 anthrax attacks and the SARS outbreak in 2003. These events require large-scale and timely dispensing of critical medical countermeasures for protection of the general population. This thesis research focuses on developing mathematical models, real-time algorithms, and computerized decision support systems that enable (1) systematic coordination to tackle multifaceted nature of mass dispensing, (2) fast disease propagation module to allow immediate mitigation response to on-site uncertainties, and (3) user-friendly platform to facilitate modeling-solution integration and cross-domain collaboration. The work translates operations research methodologies into practical decision support tools for public health emergency professionals. Under the framework of modeling and optimizing the public health infrastructure for biological and pandemic emergency responses, the task first determines adequate number of point-of-dispensing sites (POD), by placing them strategically for best possible population coverage. Individual POD layout design and associated staffing can thus be optimized to maximize throughput and/or minimize resource requirement for an input throughput. Mass dispensing creates a large influx of individuals to dispensing facilities, thus raising the risk of high degree of intra-facility infections. Our work characterizes the interaction between POD operations and disease propagation.Specifically, fast genetic algorithm-based heuristics were developed for solving the integer-programming-based facility location instances. The approach has been applied to the metro-Atlanta area with a population of 5.2 million people spreading over 11 districts. Among the 2,904 instances, the state-of-the-art specialized integer programming solver solved all except one instance to optimality within 300,000 CPU seconds and solved all except 5 to optimality within 40,000 CPU seconds. Our fast heuristic algorithm returns good feasible solutions that are within 8 percent to optimality in 15 minutes. This algorithm was embedded within an interactive web-based decision support system, RealOpt-Regional©. The system allows public health users to contour the region of interest and determine the network of PODs for their affected population. Along with the fast optimization engine, the system features geographical, demographical, and spatial visualization that facilitate real-time usage. The client-server architecture facilities front-end user interactive design on Google Maps© while the facility location mathematical instances are generated and solved in the back-end server.In the analysis of disease propagation and mitigation strategies, we first extended the 6-stage ordinary differential equation-based (ODE) compartmental model to accommodate POD operations. This allows us to characterize the intra-facility infections of highly contagious diseases during local outbreak when large dispensing is in process. The disease propagation module was then implemented into the CDC-RealOpt-POD© discrete-event-simulation-optimization. CDC-RealOpt-POD is a widely used emergency response decision support system that includes simulation-optimization for determining optimal staffing and operations. We employed the CDC-RealOpt-POD environment to analyze the interactions between POD operations and disease parameters and identified effective mitigation strategies. The disease propagation module allows us to analyze the efficient frontier between operational efficiencies and intra-POD infections. Emergency response POD planners and epidemiologists can collaborate under the familiar CDC-RealOpt-POD environment, e.g., design the most efficient plan by designing and analyzing both POD operations and disease compartmental model in a unified platform. Corresponding problem instances are formed automatically by combining and transforming graphical inputs and numerical parameters from users. To facilitate the operations of receiving, staging and storage (RSS) of medical countermeasures, we expanded the CDC-RealOpt-POD layout design functions by integrating it with the process flow. The resulting RSS system allows modeling of both system processes along with spatial constraints for optimal operations and process design. In addition, agent-based simulation was incorporated inside where integrated process flow and layout design allow analysis of crowd movement and congestion. We developed the hybrid agent behavior where individual agents make decision through system-defined process flow and autonomous discretion. The system was applied successfully to determine guest movement strategies for the new Georgia Aquarium Dolphin Tales exhibit. The goal was to enhance guest experience while mitigating overall congestion.