Zhongguo gonggong weisheng (Aug 2024)

Comparison of prediction effects of four time series models on mumps incidence rate in Jiangxi province

  • Yuqin ZHAO,
  • Shicheng GUO,
  • Hui WU,
  • Fei XU,
  • Tianchen ZHANG

DOI
https://doi.org/10.11847/zgggws1143433
Journal volume & issue
Vol. 40, no. 8
pp. 980 – 984

Abstract

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ObjectiveCompare the prediction effects of the seasonal autoregressive integrated moving average (SARIMA), exponential smoothing (ETS), trigonometric box-cox transform, ARMA residuals, trend, and seasonal components (TBATS), and neural network autoregressive (NNAR) time series models on mumps incidence rate in Jiangxi province, to provide a reference for mumps prevention and control. MethodsMumps case and incidence rate data reported in Jiangxi province from January 1, 2010 to December 31, 2019 were collected from the China Disease Prevention and Control Information System. The reported mumps incidence rates from January 2010 to December 2018 were used as the training set, and SARIMA, ETS, TBATS, and NNAR models were constructed using R 4.1.2 software to predict the mumps incidence rates from January to December 2019. Root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the fitting and prediction effects of the four models. ResultsThe reported mumps incidence rates in Jiangxi province in 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, and 2019 were 20.49/100 000, 32.03/100 000, 31.89/100 000, 19.95/100 000, 12.22/100 000, 14.10/100 000, 16.56/100 000, 16.21/100 000, 14.29/100 000, and 21.14/100 000, respectively, with an average annual reported incidence rate of 19.84/100 000 from 2010 to 2019. Mumps incidence in Jiangxi province showed obvious seasonality, with the main peak from April to July and a secondary peak from November to January of the following year. The fitted values of the four models showed consistent trends with the actual values. Except for the high MAPE (32.01%) of the SARIMA model, the MAPEs of the other three models were all < 15%, with the NNAR model having fitted values closer to the actual values and the lowest RMSE, MAE, and MAPE of 0.20, 0.14, and 8.24%, respectively. Except for the SARIMA model, the predicted value trends of the other three models were consistent with the actual values, and their MAPEs were all < 15%, with the TBATS and ETS models having the best prediction effects. ConclusionThe ETS, TBATS, and NNAR models have good fitting and prediction effects on mumps incidence rates in Jiangxi province, and the prediction results can provide theoretical guidance for mumps prevention and control in the region.

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