Optimum Transport Intervention Strategies for Influenza Pandemic Mitigation: Using Genetic Algorithm-Based Cost-Benefit Analyses

An effective region-oriented transport intervention strategy can delay the spread of a novel influenza and mitigate synchronous epidemic timing across various regions, thereby preventing infection surges. Such a strategy will also support the efficient allocation of limited medical resources, and prevent the collapse of large-scale medical systems. An important goal is giving health authorities as much time as possible to implement alternative intervention strategies. The topological features of transportation networks not only determine the epidemic dynamics of a novel influenza, but also affect the efficacies of transport intervention strategies. Inappropriately implemented transport intervention strategies can affect the commutes of large numbers of citizens, thus causing unnecessary economic losses. Health authorities must therefore carefully evaluate the pros and cons of all transport intervention strategies under consideration. For this two-year project, we will add a genetic algorithm framework to a multilayer epidemic dynamics simulator for the purpose of performing cost-benefit analyses. The ultimate goal in this project is to determine efficient implementation timing and execution order of transport intervention strategies during an influenza outbreak. We expect that our future simulation results will indicate that optimum transport intervention strategies can be identified based on epidemiological factors and implementation conditions. Our framework can also be used to assess the efficacies of other target-specific intervention strategies— for example, distributing limited quantities of vaccines and anti-viral drugs.

Project ID:PB10308-2727
External Project ID:MOST103-2221-E182-052