RAPID: Creating and Analyzing Hybrid Multiscale Models for Forecasting and Mitigating an Outbreak of the Ebola Virus Disease

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This research will create and analyze mathematical models for predicting and mitigating the spread of Ebola Virus Disease. The investigators propose to combine the strengths of three common modeling approaches by developing a new, integrative model. The result will be a more robust model that can be used to provide valuable and timely information to help control the current epidemic and inform future decision making.Standard modeling approaches such as ordinary differential equation models, network models and individual based models, have common limitations. Alone, none of these models are sufficient to capture the complexity of the 2014 Ebola epidemic. The investigators will build on existing mathematical analysis, data, and software to combine these approaches on data from the World Health Organization (WHO) as well as the Centers for Disease Control (CDC). This agent-based model will be integrated in a city and county-based network model for the migration, health care, response, and migration efforts. Specific aims for the model include forecasting the incidence of Ebola virus disease, quantifying the uncertainty in the early stages of the epidemic, estimating the spread to new regions, quantifying the impact of interventions and behavior changes, utilizing statistical analysis to estimate the degree of underreporting, quantifying the model?s uncertainty, and determining how uncertainty will impact the different mitigation approaches.