  Zoonotic diseases spread through pathogens-infected animal carriers. In the
case of Ebola Virus Disease (EVD), evidence supports that the main carriers are
fruit bats and non-human primates. Further, EVD spread is a multi-factorial
problem that depends on sociodemographic and economic (SDE) factors. Here we
inquire into this phenomenon and aim at determining, quantitatively, the Ebola
spillover infection exposure map and try to link it to SDE factors. To that
end, we designed and conducted a survey in Sierra Leone and implement a
pipeline to analyze data using regression and machine learning techniques. Our
methodology is able (1) to identify the features that are best predictors of an
individual's tendency to partake in behaviors that can expose them to Ebola
infection, (2) to develop a predictive model about the spillover risk
statistics that can be calibrated for different regions and future times, and
(3) to compute a spillover exposure map for Sierra Leone. Our results and
conclusions are relevant to identify the regions in Sierra Leone at risk of EVD
spillover and, consequently, to design and implement policies for an effective
deployment of resources (e.g., drug supplies) and other preventative measures
(e.g., educational campaigns).
