work · 2026 IPM malaria (BMGF) methodology

a geospatial seasonality upgrade for malaria models

Peak transmission month across sub-Saharan Africa, shown at four modeling resolutions: country rainfall, admin1 rainfall, water-balance, and species kernel

Peak transmission month, sharpening from one national curve to an admin1-resolution emergence surface — open the full flyover →

The portfolio’s malaria models inherited seasonality from country-level rainfall: one transmission curve per country, applied uniformly to every district inside it. This work rebuilt seasonality as an admin1-resolution, mechanism-aware surface — water-balance and species-kernel emergence models, calibrated against observed transmission and handed off cleanly to OpenMalaria.

countries

362

admin1 units

resolutions compared

monthly surfaces

Context

Seasonality drives when interventions land and how transmission rebounds. The legacy portfolio used a single national rainfall curve per country, which flattens real within-country variation — a district in the arid north and one on a humid lakeshore got the same transmission calendar. For a portfolio deciding when and where to deploy seasonal products, that resolution wasn’t good enough.

What this is

The upgrade moves seasonality through four representations, from the legacy national curve to a mechanism-based admin1 surface:

Country rainfall (status quo) — one curve per country, the legacy baseline.
Admin1 rainfall — the same rainfall driver, resolved to sub-national units.
Water-balance (admin1) — a mechanistic soil-moisture / water-balance model, not raw rainfall.
Species kernel (admin1) — a vector-emergence kernel that converts environment into monthly mosquito emergence.

Monthly vector emergence surfaces for January, April, July, and October under the species-kernel model

Monthly emergence under the species-kernel model — the seasonal signal each district actually receives.

What I owned

The full seasonality pipeline — environmental drivers to per-admin monthly emergence surfaces.
The water-balance and species-kernel emergence models and their parameterization.
Calibration against observed transmission timing, and the skill diagnostics that justify the upgrade.
The OpenMalaria handoff — turning monthly surfaces into simulator-ready seasonality inputs.
The flyover report itself: a public, reproducible walkthrough of the method and its results.

What it changes

One national curve per country became 362 admin1 emergence profiles, handed to OpenMalaria without touching the simulator. The public flyover shows where peak transmission month moves under each representation, and how each compares against observed transmission timing.

See it

The full flyover is public: read the seasonality flyover →