Prof Placide Mbala–Kingebeni contributes to a model to improve predictions of Ebola outbreaks

Dr Placide Mbala-Kingebeni, a professor with UWC’s South African National Bioinformatics Institute (SANBI), is part of an international team that has proposed an updated model for predicting where Ebola viruses could spill over from animals into humans.

In a new paper, currently in pre-print and still out for comment, the researchers explain how they integrated human activities into existing habitat suitability models (or HSMs), significantly improving on those models’ accuracy for predicting spillover events of orthoebolaviruses, the group of viruses that cause Ebola disease. HSMs use data on species occurrence and environmental factors like temperature and rainfall to estimate where zoonotic events – the jump of disease-causing pathogens from animals to humans – are most likely to occur.

But those models have to some degree overlooked an important variable, the authors argue, in that they do not account for anthropogenic drivers, i.e. human activities.

For their study, the team zeroed in on three bat species believed to serve as reservoirs – the animal host – for orthoebolaviruses, and how they triggered previous Ebola outbreaks. They then incorporated human activities like the presence and setting up of settlements, informal mining, bushmeat-related activities (hunting and consumption of bushmeat), forest loss, and conflict into their calculations.

The researcher referenced the 2026 outbreak of the Bundibugyo virus, a species of orthoebolavirus, in the Democratic Republic of the Congo (DRC) and Uganda. The spread of the disease in the Ituri province of the DRC, especially, was exacerbated by armed conflicts, food insecurity, human displacement, as well as rainforest and small-scale mining activities in the region.

Testing their model on past zoonotic spillover locations, the team found that their model’s scores improved from 0.78 to 0.97 on the widely used Boyce index, which looks at the correlation between observed and predicted occurrences of organisms. Mining- and bushmeat-based scenarios were identified as key spillover drivers. Conflict was also pinpointed as a contributor.

“This is a real game changer,” says Mbala-Kingebeni, who is also an associate professor at the University of Kinshasa Medical School in the DRC. “This new model provides a more current representation of areas with conditions suitable for the persistent and spillover of the Ebola virus, in the
form of an updated ecological niche model for this virus.”

For the work, Mbala-Kingebeni teamed up with colleagues from the Democratic Republic of the Congo, the UK, the US, and South Africa, including from Centre for Epidemic Response and Innovation (CERI) at Stellenbosch University.