An FSI-AIBR Disease Intelligence Platform
Global Malaria Watch
Supporting evidence-informed malaria surveillance, epidemiological analysis and operational decision-making across malaria-endemic regions.
The platform consolidates published malaria burden, intervention coverage, diagnostic, treatment and financing indicators for 46 sub-Saharan African countries, 2000–2024, into a single auditable time series — with every aggregation rule documented and every figure traceable to a source country-year observation.
Current position · 2024
- Estimated cases, 2024
- 270.7M
- Estimated deaths, 2024
- 594k
- Incidence per 1 000 at risk
- 210.5
- Indicator series
- 15
46 countries in scope
WHO modelled estimate
Re-derived, not averaged
2000–2024, country-year
Research Preview v0.1
2026-08-04
46 countries · 2000–2024
WHO GHO · World Bank WDI
Why this exists
Malaria data is abundant. Integrated malaria intelligence is not.
Sub-Saharan Africa carries approximately 95% of the global malaria burden. The analytical constraint is rarely the absence of data — it is fragmentation, inconsistent aggregation and the distance between a published estimate and an operational decision.
Data exists, but not in one place
Burden estimates sit in WHO Global Health Observatory releases, denominators in World Bank series, coverage in household surveys, environment in satellite archives. Assembling a single comparable view is a multi-week analytical exercise repeated independently by every ministry, donor and research group.
Aggregation is where most dashboards fail
Rates per 1 000 or per 100 000 are frequently averaged across countries rather than re-derived from summed numerators and populations. Coverage percentages are combined unweighted. The resulting regional figures are not wrong by a rounding error — they are structurally misleading.
Missing data is silently manufactured
Country-years without an observation are commonly zero-filled or interpolated, which understates burden and inflates apparent progress. This platform preserves nulls and displays the number of reporting countries behind every aggregate.
Surveillance is separated from decisions
Epidemiological outputs are rarely framed for the operational question in front of a decision-maker. Global Malaria Watch presents the same audited dataset through executive, programme and clinical lenses, and is being extended toward forecasting and scenario simulation.
Intended users
One audited dataset, four decision lenses
The same underlying observations are framed for the question in front of each user, rather than reformatted into a generic analytics view.
| User | Primary analytical use | Module |
|---|---|---|
| Ministries of health & state executives | National and sub-regional burden position, peer comparison, concentration of cases and briefing-ready figures for cabinet and parliamentary reporting. | Executive lens |
| Programme managers & implementing NGOs | Intervention coverage read against burden trajectory — ITN access and use, IPTp3, IRS protection and domestic financing on a common baseline. | Programme lens |
| Clinical & facility leadership | Diagnostic mix, test-confirmation rate and ACT treatment ratios to interpret case-management performance in epidemiological context. | Clinical lens |
| Researchers, academics & analysts | Documented indicator definitions, transparent aggregation rules, preserved missing values and full CSV export from every table. | All modules |
Product positioning
The dashboard is the first module of a disease intelligence ecosystem.
This release establishes the verified historical surveillance layer. Subsequent phases extend the same architecture to environmental and climate covariates, explainable machine-learning risk forecasting, and a digital twin for scenario simulation and intervention planning. Nothing is displayed on the platform before its data source is licensed, documented and reproducible.
Review the four-phase roadmap- ✓ Current release
Historical surveillance
WHO burden, coverage, diagnostic, treatment and financing series with World Bank denominators.
- Planned
Climate intelligence
Rainfall, temperature, vegetation and environmental monitoring as transmission covariates.
- Planned
Predictive intelligence
Machine learning and explainable AI for validated short-horizon risk forecasting.
- Planned
Digital twin
Scenario simulation, decision support and intervention planning at national scale.
Begin with the evidence
Scrub 25 years of burden and coverage, isolate any country for an intelligence briefing, and export every table as CSV for independent analysis.