Background Malaria continues to pose a public health challenge in Sierra Leone, where timely and accurate forecasting can guide more effective interventions. Although seasonal models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) have traditionally been employed for disease forecasting, Artificial Neural Networks (ANNs) have gained attention for capturing complex temporal patterns that linear models may not fully capture. Methods This study compares the forecasting performance of SARIMA and ANN models in forecasting malaria cases using malaria case data from 2018 to 2023. A baseline SARIMA model was developed and improved with exogenous climatic variables (precipitation, maximum temperature, and mean relative humidity) to form a SARIMAX approach. In parallel, an ANN was trained solely on historical malaria cases, without adding climatic variables. Results SARIMA offered reasonable predictive capabilities but was outperformed by the ANN, which captured complex temporal patterns more effectively, decreasing forecast errors and improving its coefficient of determination <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12936_2025_5389_Article_IEq1.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="33" /> </InlineMediaObject> <EquationSource Format="TEX">$(R^2)$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation>. The SARIMA model achieved an MAPE of 12.01%, which improved further to an MAPE of 11.45% with the inclusion of climatic variables. A strong positive correlation between precipitation (r = 0.68) and malaria cases was observed, while maximum temperature showed a moderate negative correlation (r = <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12936_2025_5389_Article_IEq2.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="44" /> </InlineMediaObject> <EquationSource Format="TEX">$-0.45$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>-</mo> <mn>0.45</mn> </mrow> </math></EquationSource> </InlineEquation>), and mean relative humidity demonstrated a moderate positive correlation (r = 0.55). The ANN model outperformed both the baseline SARIMA and SARIMAX models with the lowest MAPE of 6.68%. Conclusions These findings underscore the ANN’s ability to capture non-linear dynamics, even without explicit climate inputs. These results reinforce the value of machine learning modelling approaches in guiding malaria control strategies, particularly in high-burden settings like Sierra Leone.

Forecasting malaria cases using climate variability in Sierra Leone

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2025年5月20日

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Saidu Wurie Jalloh,
Boniface Malenje,
Herbert Imboga,
Mary H. Hodges

摘要

Background

Malaria continues to pose a public health challenge in Sierra Leone, where timely and accurate forecasting can guide more effective interventions. Although seasonal models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) have traditionally been employed for disease forecasting, Artificial Neural Networks (ANNs) have gained attention for capturing complex temporal patterns that linear models may not fully capture.

Methods

This study compares the forecasting performance of SARIMA and ANN models in forecasting malaria cases using malaria case data from 2018 to 2023. A baseline SARIMA model was developed and improved with exogenous climatic variables (precipitation, maximum temperature, and mean relative humidity) to form a SARIMAX approach. In parallel, an ANN was trained solely on historical malaria cases, without adding climatic variables.

Results

SARIMA offered reasonable predictive capabilities but was outperformed by the ANN, which captured complex temporal patterns more effectively, decreasing forecast errors and improving its coefficient of determination $(R^2)$ $(R^{2})$ . The SARIMA model achieved an MAPE of 12.01%, which improved further to an MAPE of 11.45% with the inclusion of climatic variables. A strong positive correlation between precipitation (r = 0.68) and malaria cases was observed, while maximum temperature showed a moderate negative correlation (r = $-0.45$ $- 0.45$ ), and mean relative humidity demonstrated a moderate positive correlation (r = 0.55). The ANN model outperformed both the baseline SARIMA and SARIMAX models with the lowest MAPE of 6.68%.

Conclusions

These findings underscore the ANN’s ability to capture non-linear dynamics, even without explicit climate inputs. These results reinforce the value of machine learning modelling approaches in guiding malaria control strategies, particularly in high-burden settings like Sierra Leone.