Global epidemic propagation occurs at multiple (local and global) scales: individuals within a subpopulation may be infected through local contacts during a local outbreak. Thus, disease spread simulations require data and models, including social contact networks, local and global mobility patterns of individuals, transmission and recovery rates, and outbreak conditions. Effectively managing the epidemic emergencies through real-time and continuous decision making requires computational models specifically tailored to the spatio-temporal dynamics of epidemics and data- and model-driven computer simulations for their spreading. Epidemic simulations track 100s of inter-dependent parameters, spanning multiple layers and geo-spatial frames, affected by complex dynamic processes operating at different resolutions. Moreover, given the unpredictability of the Ebola epidemic, decision makers need to generate ensembles, with many thousands of simulations, each with different parameters corresponding to different, but plausible, scenarios. These simulations need to be continuously revised based on real-world data as the epidemic and intervention mechanisms evolve. Tools that help running and interpreting epidemic simulation ensembles (aligned with the real-world observations) to generate timely actionable results are critically needed.  The research will result in novel algorithms and tools (namely EpiDMS) specially tailored for officials to continuously assess the impacts of different intervention scenarios and revise estimates based on real world data, at local and global scales, for the Ebola epidemic. The project results will also translate into predictions of the Ebola epidemic's characteristics, including the duration and overall size, and help the global efforts to prevent the disease from turning into a pandemic.

This NSF funded project is a collaboration with the School of Public Health at Georgia State University.