Global epidemic propagation occurs at multiple (local and global) scales: individuals within a subpopulation may be infected through local contacts during a local outbreak. These individuals then may carry the infection to a new region of the world, starting a new 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. While very powerful and highly modular and flexible epidemic spread simulation software, such as GLEaM and STEM, exist, these simulation software and existing models have been designed for highly communicable diseases, such as influenza. In contrast, Ebola spread is driven by close contact via bodily fluids in the community and transmission has been shown to be amplified in the healthcare settings or as a result of local cultural practices and rituals (such as burial practices that involve touching the body of the deceased). In contrast to influenza and other respiratory diseases, the particular epidemiological characteristics of Ebola including the high pathogenicity, disease-induced population behavior changes and infectiousness at later stages of the disease significantly affect the transmission dynamics. Existing models are yet to consider these aspects into account.
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. There are two main research thrusts: (a) Ebola specific disease simulation models, including specific transmission patterns, local social and cultural variables and evolving intervention strategies (including cost and constraints on the interventions) and their side-effects; and (b) tools for executing large-scale epidemic simulation ensembles under a large number of diverse hypotheses/scenarios and analysis, exploration, and visualization of simulation ensembles, including estimating transmissibility of Ebola, forecasting the spatio-temporal spread of Ebola at different spatial scales, assessing the cost and impact of interventions. The research results 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.
EpiDMS will fill an important hole in data-driven decision making during epidemic emergencies and, thus, will enable applications and services with significant economic and health impact. The results will 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 project is a collaboration with the School of Public Health at Georgia State University and different aspects of the projects are funded by NSF grants #1318788 and #1518939 .