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Improving Lassa fever risk mapping using self-organizing maps and spatial determinants.

Komi Mensah Agboka1, Moses Mwaura2, Bonoukpoè M Sokame2

  • 1International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772 00100, Nairobi, Kenya; Laboratoire de Recherche en Science et Technologie (LARSI), Département de Génie Informatique (GI), École Polytechnique de Lomé (EPL), Université de Lomé, Lomé, Togo.

Acta Tropica
|March 5, 2026
PubMed
Summary
This summary is machine-generated.

A new Self-Organizing Maps (SOM) model accurately predicts Lassa fever risk hotspots in West Africa. This approach integrates environmental data and human settlement patterns to enhance disease surveillance and prevention efforts.

Keywords:
EpidemiologyHuman-rodent interactionLandscapePublic healthZoonotic disease

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Area of Science:

  • Epidemiology
  • Environmental Science
  • Data Science

Background:

  • Lassa fever is a zoonotic disease prevalent in West Africa, mainly spread by the Mastomys natalensis rodent.
  • Current risk models often fail to account for human settlement and environmental interactions.
  • Improved prediction models are crucial for effective Lassa fever control.

Purpose of the Study:

  • To introduce and evaluate a Self-Organizing Maps (SOM) classification approach for enhanced Lassa fever risk prediction.
  • To integrate high-dimensional environmental data, rodent suitability, and human settlement density into a predictive model.
  • To identify recurrent risk hotspots and potential outbreak zones.

Main Methods:

  • Utilized Google Earth Engine to extract environmental predictors (1980-2022), including land surface temperature, vegetation index, evapotranspiration, elevation, and built-up areas.
  • Developed a SOM classification model incorporating environmental data, rodent occurrence, and human settlement density.
  • Validated model accuracy using training and validation datasets, achieving >0.89 accuracy.

Main Results:

  • The SOM model demonstrated high accuracy (>0.89) in predicting Lassa fever risk.
  • Identified recurrent risk hotspots that consistently aligned with historical outbreak locations.
  • Predicted moderate to high risk for Sierra Leone (100%), Liberia (100%), Guinea (97.5%), Cameroon (77.6%), and Nigeria (45.8%).

Conclusions:

  • The SOM approach offers a significant improvement over traditional regression-based models for Lassa fever risk prediction.
  • This data-driven framework enhances the ability to predict risk areas and potential outbreaks.
  • The findings support improved Lassa fever monitoring, response strategies, public awareness, control, and prevention in West Africa.