Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis

This study proposes a combined Seasonal Autoregressive Integrated Moving Average and Long Short-Term Memory (SARIMA-LSTM) model to enhance the accuracy of evaluating the effectiveness of visceral leishmaniasis prevention and control efforts in Yangquan, China.

Data were obtained from the Yangquan Centre for Disease Control and Prevention. The hybrid model integrates a SARIMA component with a residual-based LSTM neural network.

In the SARIMA-LSTM model, the LSTM component included seven hidden layer nodes, a learning rate of 0.001, 500 training epochs, a batch size of 256, and utilized the Adam optimization algorithm. The SARIMA-LSTM model demonstrated superior performance (MSE = 2.824, MAE = 1.279, RMSE = 1.681). A paired samples t-test revealed a statistically significant difference between predicted and actual case counts (t = -4.058, p < 0.001), indicating that the actual number of cases was lower than predicted.

The combined SARIMA-LSTM model outperformed the individual SARIMA and LSTM models, suggesting that the implemented interventions were generally effective.