Spatial risks of Orthoebolavirus spillover vary based on outbreak type

•We present an accurate map of Orthoebolavirus risk and test its validity.•We also show that the factors predicting spillover vary among classes of outbreaks.•For example, different factors best predict Zaire vs Sudan Orthoebolavirus spillover.•The machine learning methods we demonstrate will be of...

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Veröffentlicht in:International journal of infectious diseases Jg. 161; S. 108180
Hauptverfasser: Sundaram, Mekala, Stephens, Patrick R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Canada Elsevier Ltd 01.12.2025
Elsevier
Schlagworte:
Africa South of the Sahara - epidemiology
Animals
Communicable Diseases, Emerging - epidemiology
Disease Outbreaks
Ebolavirus disease
Ecosystem
Emerging infectious diseases
Humans
Infectious Disease
Logistic Models
Machine learning
Pulmonary/Respiratory
Risk Factors
Spillover risk
Sudan ebolavirus
Zaire ebolavirus
Africa South of the Sahara
Spillover risk
Sudan ebolavirus
Ebolavirus disease
Emerging infectious diseases
Machine learning
Zaire ebolavirus
machine-learning
emerging infectious diseases
spillover risk
Orthoebolavirus
ebolavirus disease
ISSN:1201-9712, 1878-3511, 1878-3511
Online-Zugang:Volltext
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Zusammenfassung:•We present an accurate map of Orthoebolavirus risk and test its validity.•We also show that the factors predicting spillover vary among classes of outbreaks.•For example, different factors best predict Zaire vs Sudan Orthoebolavirus spillover.•The machine learning methods we demonstrate will be of value in many emerging infectious disease systems. We develop and test a risk map for Orthoebolaviruses which are emerging infectious pathogens primarily concentrated in sub-Saharan Africa. The accuracy of predictive models and risk maps has been limited thus far by uncertainty in mechanisms underlying spread, low number of known outbreaks, and in how well various drivers predict different types of outbreaks (e.g. human vs epizootic outbreaks, and outbreaks of different viral species). Here, we explore frugivory and other factors as mechanisms of Orthoebolavirus spread and demonstrate statistical methods with repeated cross-validation that can be used even with very small data sets to explore how different factors influence different classes of events using ensemble machine learning logistic regression. We show that covariates predicting outbreaks with the highest discrimination power are frugivore richness (area under curve [AUC] = 0.95) and fruit tree (Ficus) habitat suitability (AUC = 0.94). We found that Ficus distributions contributed to predictions of past Orthoebolavirus outbreaks relatively equally, regardless of type, based on feature contributions estimated using Shapley value calculations. In contrast, frugivore richness was a better contributor of predictions of epizootic than human outbreaks. Hunting activity was a poor predictor overall (AUC = 0.85) but contributed to some Sudan outbreaks predictions. Our results suggest that different drivers best influence different classes of Orthoebolavirus outbreaks and models taking into account a variety of factors are needed to predict future spillover events.
Bibliographie:ObjectType-Article-1
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ISSN:1201-9712
1878-3511
1878-3511
DOI:10.1016/j.ijid.2025.108180