Bilevel Optimization Model for the Development of Real-Time Strategies to Minimize Epidemic Spreading Risk in Air Traffic Networks

An understanding is needed of how epidemics spread to new regions via the global air traffic network so that effective strategies for outbreak control can be developed. Various studies have focused on predicting epidemic spread via the complex air traffic network. However, there is a gap in the literature demanding real-time predictive models that exploit the heterogeneous nature of the air travel pattern to optimize decision making among a set of potential control strategies. A bilevel optimization model is proposed to solve the resource allocation problem for an ongoing epidemic spreading via the air traffic network. The upper-level objective is to optimize the distribution of limited resources for epidemic control, and the lower-level simulation model computes the risk posed to the network under possible scenarios. Results from a demonstration network highlight the advantages of this model. A case study evaluates the risk posed by Ebola to the United States through the domestic air traffic network. The results demonstrate the ability of the model to develop real-time strategies that account for the heterogeneous nature of the air traffic network and the complex dynamics of epidemic spread.