A disease surveillance & forecasting framework
This platform complements Uganda’s DHIS2 with near real-time analytics and forecasting models, helping health teams analyze aggregated surveillance data and act on it quickly.
The goal is faster insights, better decisions, and more effective interventions across regions.
Open models
Explore models you can test today
Forecast malaria and tuberculosis cases with models published by research teams. No account or coding needed.
How it works
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Researchers upload
Research teams publish a trained model together with a short description of the data it needs.
Contribute a model -
The framework validates
The framework loads the model, checks its description and accuracy figures, and builds a test form and an open API for it.
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Anyone can test
Health workers, planners and developers try models with their own aggregated data, in the browser or through the API.
Try a model
Integrated surveillance
We combine routine health facility reports, environmental data and AI analytics to help spot unusual trends early and plan prevention.
Operational decisions
We make it easier to collect, analyze, and share health data so interventions can be planned and executed with confidence.
Open collaboration
We share data and models openly so research teams and health systems can build together and move faster.
Key features
Near real-time surveillance
Follow disease trends as new reports arrive, to shorten the delay between reporting and action.
Better data collection
Support timely, complete reporting from health facilities with digital tools instead of paper records. The models only use aggregated, de-identified counts.
Spatial & temporal insights
Use location and timing to understand how environment and seasonality contribute to outbreaks.
DHIS2 integration
Enhance DHIS2 with utilities for reliable, near real-time reporting and trend analysis.
Open AI models
Test the published models in the browser or through the open API. Research teams can ask for access to the data behind them.
Why it matters
Traditional surveillance can face inaccuracies, reporting delays, and limited access to live information. The framework improves collection methods and adds advanced analytics so teams can respond faster and with greater precision.
The result is more effective control, better use of resources, and a stronger chance to save lives.
Collaborate & innovate
The models and the API on this site are open to everyone. Researchers, health organizations and governments can test them, build on them and ask us for access to the data behind them. We want a global community advancing disease surveillance together.
Join us
Explore the platform and help advance disease surveillance and forecasting. With timely data, AI-driven insights, and an open approach, we can build smarter, more responsive health systems.
