Keywords
Agent-based Modeling, Ebola, Stochastic Simulation
Disciplines
Epidemiology
Abstract
The emergence of Ebola in West Africa is of worldwide public health concern. Successful miti- gation of epidemics requires coordinated, well-planned intervention strategies that are specific to the pathogen, transmission modality, population, and available resources. Modeling and sim- ulation in the field of computational epidemiology provides predictions of expected outcomes that are used by public policy planners in setting response strategies.
Developing up to date models of population structures, daily activities, and movement has proven challenging for developing countries due to limited governmental resources. Recent collaborations (in 2012 and 2014) with telecom providers have given public health researchers access to Big Data needed to build high-fidelity models. Researchers now have access to billions of anonymized, detailed call data records (CDR) of mobile devices for several West African countries. In addition to official census records, these CDR datasets provide insights into the actual population locations, densities, movement, travel patterns, and migration in hard to reach areas. These datasets allow for the construction of population, activity, and movement models. For the first time, these models provide computational support of health related decision making in these developing areas (via simulation-based studies).
New models, datasets, and simulation software were produced to assist in mitigating the continuing outbreak of Ebola. Existing models of disease characteristics, propagation, and progression were updated for the current circulating strain of Ebola. The simulation process required the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract governmental policy level). The predictive results from this system were validated against results from the CDC's high-level predictions.
Original Citation
Vogel, N., Theisen, C., Leidig, J. P., Scripps, J., Graham, D. H., & Wolffe, G. (2015). Mining Mobile Datasets to Enable the Fine-grained Stochastic Simulation of Ebola Diffusion. Procedia Computer Science, 51, 765–774. https://doi.org/10.1016/j.procs.2015.05.197
ScholarWorks Citation
Vogel, Nicholas; Theisen, Christopher; Leidig, Jonathan P.; Scripps, Jerry; Graham, Doug H.; and Wolffe, Greg, "Mining Mobile Datasets to Enable the Fine-Grained Stochastic Simulation of Ebola Diffusion" (2015). Peer Reviewed Articles. 18.
https://scholarworks.gvsu.edu/bms_articles/18
