Key result
The SARIMA-PSO-BPNN hybrid model effectively forecasted HFMD incidence with an R-squared of 0.86, MAE of 2.89, and RMSE of 3.57, outperforming the SARIMA and SARIMA-BPNN models.
Why the study?
This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease in mainland China to support surveillance and early warning.
The SARIMA-PSO-BPNN hybrid model provides a more accurate forecast of HFMD incidence in mainland China compared to SARIMA and SARIMA-BPNN models.
May aid HFMD surveillance planning in China; leaves open whether hybrid models improve prevention outcomes.
BACKGROUND: This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD. METHODS: We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis. RESULTS: , with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively. CONCLUSIONS: Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.
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Zhao et al. (2023) studied Hand, foot, and mouth disease (HFMD) (n=144). SARIMA-PSO-BPNN hybrid model vs. SARIMA and SARIMA-BPNN models was evaluated on Prediction performance (R-squared, MAE, RMSE). The SARIMA-PSO-BPNN hybrid model effectively forecasted HFMD incidence with an R-squared of 0.86, MAE of 2.89, and RMSE of 3.57, outperforming the SARIMA and SARIMA-BPNN models.
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