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BUILD 04 — JUNE 2026

Foresight

Scoring healthcare access risk across all 774 local government areas in Nigeria, and pricing out what waiting actually costs.

PythonXGBoostscikit-learnGeoPandasStreamlit
Foresight screenshot

Drop a screenshot here:
assets/screenshots/foresight.png

State health offices in Nigeria often lack a clear view of where healthcare access gaps are most severe. Records are frequently scattered or paper-based, making it difficult to identify which areas have little to no functioning clinic access.

Foresight combines four public datasets, DHS survey data, HDX boundary files, GRID3 facility locations, and WorldPop population rasters, to score healthcare access risk across all 774 local government areas in Nigeria. For any given region, it projects the cost of delaying intervention by one, three, or five years, in terms of preventable deaths and money.

Every estimate is reported as a range rather than a single number. The model runs a Monte Carlo simulation across thousands of trials to produce low, middle, and high estimates, and regions where the model has low confidence are flagged for human review rather than scored outright.

PythonXGBoostscikit-learnGeoPandasfoliumStreamlit