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

Estimation of Ebola's spillover infection exposure in Sierra Leone based on sociodemographic and economic factors