Forecasting malaria cases using climate variability in Sierra Leone.

backgroundMalaria 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.

methodsThis 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.

resultsSARIMA 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

conclusionsThese 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.