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Mapping the risk of Guinea Worm Disease at a high resolution using geostatistical and machine learning approaches

Project Lead: Edson Utazi
Team: Shengjie Lai, Sujit K. Sahu, Peter Dorey
Funding: The Carter Center
Start date: February 2025
Completion: August 2026

The Guinea worm disease (GWD), also known as Dracunculiasis, is a neglected tropical disease widely known to affect marginalized rural communities with poor access to safe drinking water. Although great progress has been made towards its elimination, with only 14 human cases recorded in 2023, eradication has remained a challenge due to detection of cases in animals (e.g., domestic dogs and cats and baboons), rising insecurity and the difficulties posed by infections in hard-to-reach areas.

Working with the Carter Center Guinea Worm Eradication Program, we will develop novel geospatial and machine learning approaches to map the risk of GWD in six endemic sub-Saharan African countries, namely Angola, Cameroon, Chad, Ethiopia, Mali and South Sudan, to support eradication activities. The project will utilize GWD incidence data from various surveillance activities and high-quality geospatial climate, demographic, socioeconomic and environmental covariate data to produce risk maps at 1×1 km resolution, which will be aggregated to operational administrative levels through integration with gridded population data. Our analyses will characterise the environmental suitability for GWD for each endemic country and produce estimates of at-risk populations at flexible spatial scales. We will also conduct spatio-temporal analyses to assess the impact of interventions, population mobility and climate change on the spatial distribution of the disease.

In-country activities planned for the project include dissemination workshops to promote the uptake and operationalization of the research outputs among local partners, policymakers and program managers.

Project outputs:

  • 1×1 km and administrative level risk maps of GWD for the six study countries and appropriate visualizations.
  • Threshold exceedance probability maps to identify epidemiological hotspots at the grid and administrative levels and appropriate visualizations.
  • Integration of the modelled risk maps with gridded population data to quantify populations living in hotspot or high-risk areas and recommendations for targeting interventions.
  • Results of analyses exploring the impact of interventions, population mobility and climate change on GWD distribution.