Understanding the population dynamics of reservoirs of zoonotic diseases, such as bats, is a crucial first step to predict and prevent potential spillover of deadly viruses like Ebola. Due to the limited data on bats across Africa, their density and migrations can be studied with probabilistic numerical models based on samples of the ecological bare carrying capacity. To this purpose, the bare carrying capacity will be modeled as a random field and its statistics calibrated with the available data. The most popular methods for the calibration of the correlation of a random field are not applicable in the case of unevenly spaced data. We propose to use a least square regression model to estimate the autocorrelation function and remedy the problem of unevenly spaced data. The residuals (i.e., the differences between the predicted and actual values) of the regression model determining the bare carrying capacity were found to be weakly homogeneous across Africa. Correlation lengths of the residuals were found to be different along longitude and latitude. Along the longitude, the correlation length is 1.78 degrees (approximately 200 km) whereas along the latitude it is 1.40 degrees (approximately 150 km), which is expected as the climate and other parameters determining the carrying capacity change more rapidly along the latitude. The bare carrying capacity of bats was found to be more dense in central Africa. This is due to the fact that climatic and environmental conditions are more suitable for the survival of bats.

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).