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This study aims to predict rabies outbreaks in Africa using machine learning models applied to epidemiological and environmental data. Accurate forecasting of rabies outbreaks is essential for mitigating the disease's impact and saving lives. Rabies, a fatal zoonotic disease, disproportionately affects rural populations in low-resource regions, making reliable prediction crucial for public health interventions. This research evaluates the performance of several models, including RandomForestClassifier, XGBClassifier, LightGBM, and an Ensemble Model, selected for their ability to handle imbalanced datasets and complex variables. Results demonstrate that the Ensemble Model achieved the highest classification accuracy of 74.00%, while Linear Regression provided the best performance in predicting the number of cases with an R2 of 0.7916. These findings suggest that machine learning models effectively predict rabies outbreaks, providing essential insights for optimizing vaccination efforts and outbreak control measures.

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Machine Learning-Based Prediction of Rabies Outbreaks Using Epidemiological and Environmental Data in Africa

  • 会议论文

  • 发表日期：

    2025年

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  • Akwasi Asare,
  • Justice Williams Asare,
  • Seth Alornyo,
  • Emmanuel Akwah Kyei,
  • Emmanuel Freeman,
  • William Leslie Brown-Acquaye,
  • Martin Mabeifam Ujakpa,
  • Israel Edem Agbehadji,
  • Richard C. Millham

摘要

This study aims to predict rabies outbreaks in Africa using machine learning models applied to epidemiological and environmental data. Accurate forecasting of rabies outbreaks is essential for mitigating the disease's impact and saving lives. Rabies, a fatal zoonotic disease, disproportionately affects rural populations in low-resource regions, making reliable prediction crucial for public health interventions. This research evaluates the performance of several models, including RandomForestClassifier, XGBClassifier, LightGBM, and an Ensemble Model, selected for their ability to handle imbalanced datasets and complex variables. Results demonstrate that the Ensemble Model achieved the highest classification accuracy of 74.00%, while Linear Regression provided the best performance in predicting the number of cases with an R2 of 0.7916. These findings suggest that machine learning models effectively predict rabies outbreaks, providing essential insights for optimizing vaccination efforts and outbreak control measures.