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Background <p>In Senegal, malaria remains endemic, with a highly heterogeneous distribution across the country. Despite all the interventions implemented by the National Malaria Control Program (NMCP), the South-East of the country alone accounts for 78.5% of cases and 43.6% of deaths in 2021. This situation requires new approaches to better understand and anticipate the dynamics of malaria. This study aims to develop a machine learning model that can predict the incidence of malaria in the south-east districts. By providing accurate and targeted information, this model will enable the NMCP to optimize the deployment of interventions in South-East Senegal.</p> Methods <p>The study is based on 936 observations, covering the period from January 2016 to December 2021, and encompassing 13 health districts in the South-East regions of Senegal. These monthly data include detailed information on malaria cases as well as climatic, demographic and environmental characteristics. Data were divided into three sets: a training set, a testing set, and a validation set. We compared predictions of three machine learning models: Extreme Gradient Boosting (XGBoost), Histogram Gradient Boosting (HistGB) and Light Gradient Boosting Machine (LightGBM). The performance of the models was evaluated using performance metrics, while the Shapley values were used to analyze the impact and importance of the variables in the predictions.</p> Results <p>The results showed that XGBoost is the best performing model in terms of score. Overall, the model achieved a coefficient of determination (R<sup>2</sup>) of 0.90 across all 13 health districts. In particular, it demonstrated excellent precision in the districts of Kidira, Vélingara and Koumpentoum, where the predictions were very close to the observed values. Thus, this designed model provides forecasts, offering valuable information for detecting early warning signals to anticipate and reduce the impact of malaria epidemics.</p> Conclusion <p>This study predicts the incidence of malaria in a region where existing strategies are struggling to reduce the incidence thereof. This is done by accurately predicting transmission peaks that will help to better target the allocation of resources and increase the effectiveness of actions to combat the disease.</p>

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Forecasting malaria incidence in the Southeast districts of Senegal using a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月5日

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  • Almamy Youssouf Ly,
  • Mouhamad M. Allaya,
  • Mor Absa Loum,
  • Lucien D. Gning,
  • Ousmane Sy,
  • Léontine N. Bakhoum,
  • Khady Ndiaye,
  • Médoune Ndiop,
  • Fatimata Bintou Sall,
  • Jean Louis A. Ndiaye,
  • Mamadou Bousso

摘要

Background

In Senegal, malaria remains endemic, with a highly heterogeneous distribution across the country. Despite all the interventions implemented by the National Malaria Control Program (NMCP), the South-East of the country alone accounts for 78.5% of cases and 43.6% of deaths in 2021. This situation requires new approaches to better understand and anticipate the dynamics of malaria. This study aims to develop a machine learning model that can predict the incidence of malaria in the south-east districts. By providing accurate and targeted information, this model will enable the NMCP to optimize the deployment of interventions in South-East Senegal.

Methods

The study is based on 936 observations, covering the period from January 2016 to December 2021, and encompassing 13 health districts in the South-East regions of Senegal. These monthly data include detailed information on malaria cases as well as climatic, demographic and environmental characteristics. Data were divided into three sets: a training set, a testing set, and a validation set. We compared predictions of three machine learning models: Extreme Gradient Boosting (XGBoost), Histogram Gradient Boosting (HistGB) and Light Gradient Boosting Machine (LightGBM). The performance of the models was evaluated using performance metrics, while the Shapley values were used to analyze the impact and importance of the variables in the predictions.

Results

The results showed that XGBoost is the best performing model in terms of score. Overall, the model achieved a coefficient of determination (R2) of 0.90 across all 13 health districts. In particular, it demonstrated excellent precision in the districts of Kidira, Vélingara and Koumpentoum, where the predictions were very close to the observed values. Thus, this designed model provides forecasts, offering valuable information for detecting early warning signals to anticipate and reduce the impact of malaria epidemics.

Conclusion

This study predicts the incidence of malaria in a region where existing strategies are struggling to reduce the incidence thereof. This is done by accurately predicting transmission peaks that will help to better target the allocation of resources and increase the effectiveness of actions to combat the disease.