Modeling and Optimizing the Public-Health Infrastructure for Emergency Response
Eva K. Lee,
Chien-Hung Chen,
Ferdinand Pietz,
Bernard Benecke
Center for Operations Research in Medicine and HealthCare, School of Industrial and Systems Engineering, Georgia Institute of Technology, and NSF I/UCRC Center for Health Organization Transformation, Georgia Institute of Technology, Atlanta, Georgia 30332
Center for Operations Research in Medicine and HealthCare, School of Industrial and Systems Engineering, Georgia Institute of Technology, and NSF I/UCRC Center for Health Organization Transformation, Georgia Institute of Technology, Atlanta, Georgia 30332
Strategic National Stockpile, Coordinating Office for Terrorism Preparedness and Emergency Response, Centers for Disease Control and Prevention, Atlanta, Georgia 30333
Strategic National Stockpile, Coordinating Office for Terrorism Preparedness and Emergency Response, Centers for Disease Control and Prevention, Atlanta, Georgia 30333
eva.lee{at}gatech.edu
cchen{at}isye.gatech.edu
Public-health emergencies, such as bioterrorist attacks or pandemics, demand fast, efficient, large-scale dispensing of critical medical countermeasures. By combining mathematical modeling, large-scale simulation, and powerful optimization engines, and coupling them with automatic graph-drawing tools and a user-friendly interface, we designed and implemented RealOpt©, a fast and practical emergency-response decision-support tool. RealOpt allows public-health emergency coordinators to (1) determine locations for point-of-dispensing (POD) facility setup; (2) design customized and efficient floor plans for PODs via an automatic graph-drawing tool; (3) determine required labor resources and provide efficient placement of staff at individual stations within a POD; (4) perform disease-propagation analysis, understand and monitor the intra-POD disease dilemma, and help to derive dynamic response strategies to mitigate casualties; (5) assess resources and determine minimum needs to prepare for treating their regional populations in emergency situations; (6) carry out large-scale virtual drills and performance analyses, and investigate alternative strategies; and (7) design a variety of dispensing scenarios that include emergency-event exercises to train personnel. These advanced and powerful computational strategies allow emergency coordinators to quickly analyze design decisions, generate feasible regional dispensing plans based on best estimates and analyses available, and reconfigure PODs as an event unfolds. The ability to analyze planning strategies, compare the various options, and determine the most cost-effective combination of dispensing strategies is critical to the ultimate success of any mass dispensing effort.
Key Words: public health; emergency response; mass dispensing; resource allocation; facility location; disease propagation; medical countermeasures; bioterrorism; pandemic; infectious disease; anthrax; disaster medicine; all-hazard emergency response; public-health informatics; integer programming; simulation; decision-support system
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