The main goal of the project is to provide the scientific community with a set of quantitative modeling tools that will assist in the real-time identification of factors contributing most to the ongoing spread of Ebola in Africa, and that will allow straightforward testing of plausible intervention strategies. This will be done by developing branching process models of the spread of Ebola that connect to available data sources, and that allow for changes in human behavior as well as incorporate realistic scenarios aimed at mitigating transmission. Estimates of the key transmission rates and associated measures of uncertainty will allow for improved precision in forecasting under a range of scenarios. While multi-type branching process models are an ideal way to capture key heterogeneities in transmission, and can readily include the dynamic landscape associated with serious disease threats unfolding in real time, their potential is maximized only when we develop techniques to interface them with data. This is a two-way communication in which existing data allows estimation of critical process rates and probabilities, and in addition, the accurately parameterized models allow forecasting. For the ongoing Ebola outbreak in West Africa, the utility of both wings of this workflow have important implications. More broadly, multi-type branching process models have wide applicability to emerging infectious disease threats. Improving fitting techniques for such models fills a knowledge gap regarding the connection between a flexible modeling framework and available data sources. By identifying simple, key heterogeneities in transmission, the modeling approach transcends purpose-built models that are not easily extended to other systems. Models and associated fitting procedures for the analysis of data related to the Ebola disease will be relevant also for future emerging disease threats.


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