The goal of this project is to combine methodologies from probabilistic regional hazard modeling, non-homogeneous virus spreading, and big-data analysis to predict the probability of Ebolavirus reaching various cities of a broad geographic region. Therefore, it will be possible to deploy proactively the appropriate countermeasures at the locations where they will be most effective. A novel technique called “Functional Quantization”, previously used for the efficient and accurate probabilistic assessment of the regional distribution of natural disasters, will be adapted and applied to the distribution of resources that drive the migration patterns of the main Ebola zoonosis: bats. Similarly, a Susceptible-Infected-Recovered (SIR) model of bats ecology will be developed and coupled with a model to predict (in a probabilistic sense) the transmission of the virus from bats to humans.

PIs: Paolo Bocchini (CEE), Javier Buceta (ChemBE)