ArticleDigital health
Predictors of disease outbreaks at continental-scale in the African region: Insights and predictions with geospatial artificial intelligence using earth observations and routine disease surveillance data.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence for climate-health early warning systems in the Horn of Africa: opportunities, challenges, and a roadmap for action.Globalization and health · 2026Article
- Cautionary lessons from real-world testing of GPT-4.1 AI for pediatric foreign body aspiration.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Feasibility of AI-driven disease surveillance systems at international airports in sub-Saharan Africa: a narrative review.Frontiers in public health · 2026Review
- Advancing epidemic intelligence: evaluating Senegal's mpox surveillance system and readiness for AI-driven predictive modelling.Frontiers in public health · 2026Article
- Mapping Malaria Susceptibility in the Oti Region of Ghana Using Remote Sensing and GIS: A Retrospective Observational Study.Health science reports · 2025Article
- Artificial intelligence in clinical toxicology in Africa: Emerging applications and barriers.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2025Review
- Advances in Animal Disease Surveillance and Information Systems and Their Role in Disease Control and Prevention: Implications in Ethiopia.Veterinary medicine and science · 2025Review
- Global, regional, and national temporal trend and patterns of change in the burden of leishmaniasis from 1990 to 2021: an analysis of the Global Burden of Disease Study 2021.Science in One Health · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Objectives: Our research adopts computational techniques to analyze disease outbreaks weekly over a large geographic area while maintaining local-level analysis by incorporating relevant high-spatial resolution cultural and environmental datasets. The abundance of data about disease outbreaks gives scientists an excellent opportunity to uncover patterns in disease spread and make future predictions. However, data over a sizeable geographic area quickly outpace human cognition. Our study area covers a significant portion of the African continent (about 17,885,000 km Methods: We first applied global and local spatial autocorrelation for malaria, cholera, meningitis, and yellow fever case counts. We then used machine learning to predict the weekly presence of these diseases in the second-level administrative district. Lastly, we used machine learning feature importance methods on the variables that affect spread. Results: Our spatial autocorrelation results show that geographic nearness is critical but varies in effect and space. Moreover, we identified many interesting hot and cold spots and spatial outliers. The machine learning model infers a binary class of cases or none with the best Conclusions: Our study shows that data analytics and machine learning are vital to understanding and monitoring disease outbreaks locally across vast areas. The speed at which these methods produce insights can be critical during epidemics and emergencies.
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