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

TL;DR

This study develops a predictive model linking sociodemographic and economic factors to Ebola spillover risk in Sierra Leone, enabling targeted interventions and resource allocation to prevent outbreaks.

Contribution

It introduces a novel methodology combining survey data, regression, and machine learning to map Ebola spillover risk based on sociodemographic and economic factors.

Findings

Identified key predictors of risky behaviors for Ebola exposure.

Created a calibrated spillover risk map for Sierra Leone.

Provided insights for targeted public health interventions.

Abstract

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).