Journal of Shandong University (Health Sciences) ›› 2018, Vol. 56 ›› Issue (6): 76-82.doi: 10.6040/j.issn.1671-7554.0.2017.1185

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Comparison of multiple seasonal ARIMA model and generalized regression neural network model in forecasting the incidence of brucellosis

MA Jie, TIAN Ye, HUANG Lu, MENG Weijing, WANG Suzhen, SHI Fuyan   

  1. School of Public Health and Management, Weifang Medical University, Weifang 261053, Shandong, China
  • Published:2022-09-27

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Abstract: Objective To explore suitable model for brucellosis incidence forecasting in China, and to provide reference for forecasting warning system of brucellosis. Methods Autoregressive integrated moving average(ARIMA)model and generalized regression neural network(GRNN)model were fitted with data monthly reported by China Centers for Disease Control from January 2011 to December 2016. The monthly reported data from January to August 2017 were used to evaluate forecast results. The mean absolute error(MAE)and mean relative error(MRE)were evaluated by comparing the actual incidence with the predicted incidence of the two models. Results The MAE and MRE of the ARIMA(0,1,1)(0,1,1)12model were 989, 0.23 and the GRNN model were 561, 0.14, respectively. The MAE and MRE of the GRNN model were less than the ARIMA model. Conclusion Both the ARIMA and GRNN model perform well in forecasting the incidence of brucellosis, while the prediction ability of GRNN model is slightly better than ARIMA model.

Key words: Autoregressive integrated moving average, Multiple seasonal model, Brucellosis, Generalized regression neural network model, Time series, Forecast

CLC Number: 

  • R183
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