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Forecasting malaria cases using climate variability in Sierra Leone
Saidu Wurie Jalloh1,2, Boniface Malenje3, Herbert Imboga3
1Department of Mathematics (Data Science Option), Pan African University Institute for Basic Sciences Technology and Innovation, Kiambu, 00200, Juja, Kenya. wurie.saidu@students.jkuat.ac.ke.
Malaria Journal
|May 20, 2025
Summary
Artificial Neural Networks (ANNs) significantly outperform traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) models for malaria forecasting in Sierra Leone. ANNs provide more accurate predictions, crucial for effective public health interventions against malaria.
Area of Science:
- Epidemiology
- Machine Learning
- Public Health
Background:
- Malaria remains a significant public health concern in Sierra Leone.
- Accurate malaria forecasting is essential for effective intervention strategies.
- Traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) models have limitations in capturing complex disease patterns.
Purpose of the Study:
- To compare the forecasting performance of SARIMA and Artificial Neural Network (ANN) models for malaria cases in Sierra Leone.
- To evaluate the impact of climatic variables on malaria forecasting accuracy.
- To determine the most effective modeling approach for malaria prediction in high-burden settings.
Main Methods:
- Developed a baseline SARIMA model and a SARIMAX model incorporating precipitation, maximum temperature, and mean relative humidity.
- Trained an Artificial Neural Network (ANN) model using historical malaria case data.
- Compared the forecasting accuracy of SARIMA, SARIMAX, and ANN models using Mean Absolute Percentage Error (MAPE) and coefficient of determination.
Main Results:
- The ANN model achieved the lowest Mean Absolute Percentage Error (MAPE) of 6.68%, outperforming both SARIMA (12.01%) and SARIMAX (11.45%) models.
- A strong positive correlation was found between precipitation and malaria cases (r = 0.68).
- The ANN model demonstrated superior ability in capturing complex, non-linear temporal patterns in malaria incidence.
Conclusions:
- Artificial Neural Networks (ANNs) are highly effective for malaria forecasting, even without explicit climate data inputs.
- Machine learning approaches, like ANNs, offer significant value for enhancing malaria control strategies in high-burden regions.
- The study highlights the potential of ANNs to improve the accuracy and timeliness of disease forecasting for public health decision-making.
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