Advancing Public Health and Medical Preparedness with Operations Research

Abstract

Planning for a catastrophe involving a disease outbreak with the potential for mass casualties is a significant challenge for emergency managers. Public health experts at the US Centers for Disease Control and Prevention (CDC) teamed with operations researchers to address important aspects of mass dispensing: medical supply distribution, locations of dispensing facilities, optimal facility staffing and resource allocation, routing of the population, and dispensing methods. Simulation-optimization technology was integrated into a decision support and data management suite, RealOpt©, for tactical and strategic operational planning. RealOpt has enabled the CDC to provide modern tools that support dynamic planning for emergencies and that establish a knowledge data bank to provide feedback about the deployment of various techniques. The RealOpt suite now has a US user base of over 6,500 public health and emergency directors covering all states, plus many international users. RealOpt has been applied in hundreds of drills and dispensing events, including anthrax preparedness, and for seasonal flu and H1N1 vaccination events.

An Interactive Web-based Decision Support System for Mass Dispensing, Emergency Preparedness, and Biosurveillance

“…For clients, the Google Maps API [9] along with a webpage are incorporated to provide the primary I/O functionalities. On the server, a rapid hybrid large-scale algorithm that couples features of a combinatorial genetic algorithm and an adaptive greedy approach is implemented as a solution engine for optimization of the network of facility sites and population ow and assignment [22]. e intensive optimization computation is performed on the server, and requires no extra computing e ort from the client.…”

“…Population Flow and Assignment [16,22,23] Once a region is selected/drawn, users can input potential PODs by loading a le with addresses, input the addresses interactively, or simply drag-and-drop the POD markers onto the region. Physical constraints on the facility must be modeled, e.g., capacity of a facility cannot be violated (e.g., parking capacity, re codes, and building regulations limit the total number of individuals that can be present simultaneously inside a facility).…”

“…Using recent computational advances for solving intractable facility-like instances [24], we were able to solve instances ranging from 200,000 to 10 million variables within 40,000 CPU seconds. To facilitate strategic and operational planning and real-time dynamic decision analysis, we design a novel rapid solution engine that couples a combinatorial genetic algorithm with an adaptive greedy search [22]. e optimization heuristic schema is shown in Figure 5.…”

“…Large-scale dispensing of medical countermeasures has proven to be an e ective way to contain outbreaks of highly infectious diseases, such as smallpox, pneumonic plague, and pandemic u [14,30]. Further, successful dispensing operations, such as optimizing POD operations through RealOpt-POD, can help reduce resource requirements, shorten patient waiting time and queues, and maximize throughput [21,22]. However, the large in ux of individuals brought into these dispensing centers raises the potential risk of intra-facility cross-infections.…”

Business Analytics: Research and Teaching Perspectives

“…Eventual benefits of this model in 2011 include a $1.3 million revenue due to time saving and $3.2 million revenue due to fuel saving. Lee et al (2013) describe use of similar techniques in healthcare [8]. They used Optimization and simulation techniques to build a decision support system that would help in deploying large-scale medical counter measures in the case of a public health emergency such as disease outbreak.…”