Bernard Benecke

Modeling and Optimizing the Public-Health Infrastructure for Emergency Response

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.

Advancing Public Health and Medical Preparedness with Operations Research

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.

Vaccine Prioritization for Effective Pandemic Response

Public health experts agree that the best strategy to contain a pandemic, where vaccination is the prophylactic treatment but vaccine supply is limited, is to give higher priority to higher-risk populations. We derive a mathematical decision framework to track the effectiveness of prioritized vaccination through the course of a pandemic. Our approach couples a disease-propagation model with a vaccine queueing model and an optimization engine to determine optimal prioritized coverage in a mixed-vaccination strategy. This demonstrably minimizes infection and mortality. Given estimated outbreak characteristics, vaccine inventory levels, and individual risk factors, the study reveals an optimal coverage for the high-risk group that results in the lowest overall attack and mortality rates. This knowledge is critical to public health policy makers for determining the best strategies for population protection. This becomes particularly important in determining when to switch from a prioritized strategy emphasizing high-risk groups to a nonprioritized strategy in which the vaccine becomes available publicly. Our analysis highlights the importance of uninterrupted vaccine supply. Although the 2009 H1N1 supply, received in interrupted batches, eventually covered over 30 percent of the population, the resulting attack and mortality rates are significantly inferior to those in a scenario where only 20 percent of the population is covered with an uninterrupted supply. We also learned that early vaccination is important. Contrasting the 2009 H1N1 supply to a 10 percent uninterrupted supply, a three-week delay in vaccination reduces the 9.9 percent infection reduction of the former to a mere 0.9 percent. The optimal trigger for switching from prioritized to nonprioritized vaccination is sensitive to infectivity and vulnerability of the high-risk groups. Our study further underscores the importance of throughput efficiency in dispensing and its effects on the overall attack and mortality rates. The more transmissible the virus is, the lower the threshold for switching to nonprioritized vaccination. Our model, which can be generalized, allows the incorporation of seasonality and virus mutation of the biological agents. The system empowers policy makers to make the right decisions at the appropriate time to save more lives, better utilize limited resources, and reduce the health-service burden during a pandemic event.

Service Networks for Public Health and Medical Preparedness: Medical Countermeasures Dispensing and Large-Scale Disaster Relief Efforts

A catastrophic health event, such as a terrorist attack with a biological agent, a naturally occurring pandemic, or a calamitous meteorological or geological event, could cause tens or hundreds of thousands of casualties, weaken the economy, damage public morale and confidence, create panic and civil unrest, and threaten national security. It is therefore critical to establish a strategic vision that will enable a level of public health and medical preparedness sufficient to address a range of possible disasters. Planning for a catastrophe involving a disease outbreak or mass casualties is an ongoing challenge for first responders and emergency managers. They must make critical decisions on treatment distribution points, staffing levels, impacted populations and potential impact in a compressed window of time when seconds could mean life or death. Some of the key areas of public health and medical preparedness include medical surge, population protection, communication infrastructure, and emergency evacuation. This chapter highlights our own experience on projects with the Centers for Disease Control and Prevention and various public health jurisdictions in emergency response and medical preparedness for mass dispensing for disease prevention and treatment and large-scale disaster relief efforts.

Machine Learning for Predicting Vaccine Immunogenicity

The ability to predict how different individuals will respond to vaccination and to understand what best protects individuals from infection greatly facilitates developing next-generation vaccines. It facilitates both the rapid design and evaluation of new and emerging vaccines and identifies individuals unlikely to be protected by vaccine. We describe a general-purpose machine-learning framework, DAMIP, for discovering gene signatures that can predict vaccine immunity and efficacy. DAMIP is a multiple-group, concurrent classifier that offers unique features not present in other models: a nonlinear data transformation to manage the curse of dimensionality and noise; a reserved-judgment region that handles fuzzy entities; and constraints on the allowed percentage of misclassifications.Using DAMIP, implemented results for yellow fever demonstrated that, for the first time, a vaccine’s ability to immunize a patient could be successfully predicted (with accuracy of greater than 90 percent) within one week after vaccination. A gene identified by DAMIP, EIF2AK4, decrypted a seven-decade-old mystery of vaccination. Results for flu vaccine demonstrated DAMIP’s applicability to both live-attenuated and inactivated vaccines. Results in a malaria study enabled targeted delivery to individual patients.Our project’s methods and findings permit highlighting and probabilistically prioritizing hypothesis design to enhance biological discovery. Moreover, they guide the rapid development of better vaccines to fight emerging infections, and improve monitoring for poor responses in the elderly, infants, or others with weakened immune systems. In addition, the project’s work should help with universal flu-vaccine design.