Modelling forest degradation and risk of disease outbreaks in mainland Equatorial Guinea

Background: Epidemiologists have testified for a rise of emerging infectious diseases (EID) events in the tropical areas undergoing forest clearance episodes and make the case for their association to forest degradation and loss. In view of this, we developed a workflow of analyses based on open data to model the EID risk in a context of forest degradation, integrating both exposure to hazardous areas and vulnerability to EID. We applied the workflow to mainland Equatorial Guinea to assess population at risk and understand how anthropic activities overlap with hazardous areas. Methods: We first modelled areas associated to spillover risk by conducting a spatio-temporal analysis of deforestation over the 2010–2014 period, and a modelling of ecotones considering forest margins and areas of transitional fragmentation. We modelled the exposure to hazardous areas as the proximity to deforested areas and ecotones. Second, we modelled the lack of accessibility to hospitals to represent the vulnerability to EID. Finally, we produced an index of EID risk combining exposure to hazardous areas and vulnerability to EID. Complementarily, we mapped the interfaces between hazardous areas and anthropic activities by overlaying forest degradation areas with anthropic activities to gain insights about their overlap. Results: Our results highlight the areas where population is particularly exposed to hazardous areas and are vulnerable to the EID risk in light of their remoteness from health facilities. Zonal statistics using high- resolution population distribution revealed that 100% of Equatorial Guinea’s population is located within 15 minutes from the nearest hazardous areas, and that 92.2% stands within 1 hour from the closest hospital. Most of the population is located within the lowest EID risk levels, but 10.7% of the population is exposed to medium and high EID risk, with a set of settlements that could be targeted by health monitoring. Conclusions: Our high-resolution geospatial methodology translates anthropic impact on ecosystems and accessibility to health infrastructures into an EID risk analysis. We demonstrated that it is possible to use open data to that end, providing maps for health and environmental monitoring that can be adapted in other countries to other specific types of hazards and vulnerability.