|
|
|
| Comparison of the predictive performance of SARIMA, Prophet, and BSTS models in forecasting the incidence of hand, foot, and mouth disease |
| LU Wenhai1, KONG Xiaojie2, SONG Lixia3, LU Chunru1, YU Bikun1, XIE Yan2
|
1. Pingdi Street Public Health Service Center, Longgang District, Shenzhen, Guangdong 518117, China;
2. The Third People's Hospital of Futian District, Shenzhen, Guangdong 518029, China;
3. Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518054, China |
|
|
|
|
Abstract Objective To compare the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) model, the Prophet model, and the Bayesian structural time series (BSTS) model in forecasting the incidence of hand, foot, and mouth disease (HFMD) , so as to provide a basis for optimizing the early warning system of this disease. Methods Weekly incidence data of HFMD in Longgang District, Shenzhen City from 2014 to 2024 were collected. The HFMD incidence data from 2014-2019 and 2023 were used as the training set to construct SARIMA, Prophet, and BSTS models, while the data from 2024 were used as the test set to compare and evaluate the predictive performance of the three models. The technique for order preference by similarity to ideal solution (TOPSIS) method was employed to calculate the C-value. This approach integrates multiple evaluation metrics, such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE), to comprehensively assess model performance. Results A total of 150 111 cases of HFMD were reported in Longgang District from 2014 to 2024, with an average annual incidence of 400.72/105. The weekly incidence fluctuated between 0 and 63.78/105, exhibiting a bimodal seasonal pattern characterized by a primary peak from May to July and a secondary peak from September to October. In the training set, all three models demonstrated a good fit to the bimodal epidemic trend of HFMD, with the BSTS model achieving the best fit. The BSTS model yielded performance metrics as follows: MAE=0.124, MSE=0.050, RMSE=0.223, SMAPE=0.021, and a C-value of 1.000. In the test set, all three models, including SARIMA, Prophet, and BSTS, performed well for short-term predictions (≤16 weeks), with the Prophet model showing relatively superior predictive performance. However, the prediction accuracy of all models declined as the forecast horizon extended. During the primary peak period (May-July), the Prophet model exhibited better predictive performance, whereas the BSTS model performed relatively better during the secondary peak period (September-October). Conclusions For the short-term forecasting of weekly HFMD incidence, the Prophet model outperformed both the SARIMA and BSTS models. During the primary peak period, the Prophet model demonstrated superior predictive performance, whereas the BSTS model exhibited better accuracy in forecasting the secondary peak period.
|
|
Received: 01 July 2025
Revised: 11 November 2025
Published: 26 January 2026
|
|
|
|
|
|
[1] ESPOSITO S,PRINCIPI N.Hand,foot and mouth disease:current knowledge on clinical manifestations,epidemiology,aetiology and prevention[J].Eur J Clin Microbiol Infect Dis,2018,37(3):391-398.
[2] 张儒豪,光旭,何逸飞,等.2013—2022年广东省深圳市手足口病流行特征和病原学分析[J].疾病监测,2024,39(10):1263-1269.
[3] 宋丽霞,卢文海,张振,等.SARIMA及其组合模型对手足口病发病率预测效果比较[J].现代预防医学,2025,52(8):1490-1496,1516.
[4] TAYLOR S J,LETHAM B.Forecasting at scale[J].Am Stat,2018,72(1):37-45.
[5] ZHANG L,YANG F,ZHU Z H,et al.Assessing the impact of COVID-19 interventions on the hand,foot and mouth disease in Guangdong Province,China:a Bayesian modeling study[J/OL].Front Public Health,2024,11[2025-11-11].https://doi.org/10.3389/fpubh.2023.1307321.
[6] 张小乔,张筱碟,赵振希,等.LSTM、SARIMA和SARIMAX模型预测手足口病发病率的效果比较[J].预防医学,2025,37(3):280-283,287.
[7] 卢文海,宋丽霞,熊华威,等.基于Prophet模型的深圳市手足口病日发病率趋势分析与预测[J].中国公共卫生,2025,41(5):585-591.
[8] ZHANG F Q,LI Y Y,LI X X,et al.Comparison of ARIMA and Bayesian structural time series models for predicting the trend of syphilis epidemic in Jiangsu Province[J].Infect Drug Resist,2024,17:5745-5754.
[9] XIE C,WEN H Y,YANG W W,et al.Trend analysis and forecast of daily reported incidence of hand,foot and mouth disease in Hubei,China by Prophet model[J/OL].Sci Rep,2021,11(1)[2025-11-11].https://doi.org/10.1038/s41598-021-81100-2.
[10] LI P H,RUI J,NIU Y,et al.Analysis of HFMD transmissibility among the whole population and age groups in a large city of China[J/OL].Front Public Health,2022,10[2025-11-11].https://doi.org/10.3389/fpubh.2022.850369.
[11] 刘艳,张飞燕,段海平,等.基于分布式滞后非线性模型分析青岛市气象因素与手足口病发病间关联[J].现代预防医学,2025,52(1):63-68,81.
[12] 高琦,刘志东,王舒姿,等.广东省气温对手足口病发病影响及归因风险评估[J].中国公共卫生,2022,38(2):203-208.
[13] 王铭韩,胡泽鑫,冯录召,等.新型冠状病毒感染疫情后我国季节性流感的流行趋势与防控建议[J].中华医学杂志,2024,104(8):559-565.
[14] LUO Z X,JIA X C,BAO J Z,et al.A combined model of SARIMA and Prophet models in forecasting AIDS incidence in Henan Province,China[J/OL]. Int J Environ Res Public Health,2022,19(10)[2025-11-11].https://doi.org/10.3390/ijerph19105910. |
| [1] |
ZHANG Xiaoqiao, ZHANG Xiaodie, ZHAO Zhenxi, XIE Pengliu, DAI Min. Comparison of the prediction effects of LSTM, SARIMA and SARIMAX models on the incidence of hand, foot, and mouth disease[J]. Preventive Medicine, 2025, 37(3): 280-284,287. |
|
|
|
|
