Effective real-time allocation of pandemic interventions

We address the integration of computational laboratories, spatial agent-based simulation, and real time situation updates to provide pandemic risk assessments and optimal intervention and prevention strategies. Our goal is to support decisions that save lives by helping to integrate real-time feedback and coordinate effective responses. Computational laboratories using super computing resources allow us to explore and optimize deployments of scarce resources and disruptive interventions for controlling pandemic influenza. We have developed an agent based model for simulating the diffusion of pandemic influenza via carefully calibrated inter-city airline travel. This and related simulation models at community scales can be used to learn vital lessons based on CPU-intensive virtual experience from millions of simulated pandemics. Real-time situation updates can greatly enhance the strategic usefulness of simulation models by providing accurate interim conditions for adapting effective deployments of interventions as a pandemic unfolds.