Predicting herpes zoster incidence using a combined seasonal autoregressive integrated moving average and back propagation neural network model: A time series analysis - 03/01/25
Doi : 10.1016/j.jinf.2024.106378
Chenlu Fan 1, Kangjun Xu 1, Zhexin Xu, Chuanxi Fu ⁎ 
The Institute of Infectious Disease and Vaccine, School of Public Health, Zhejiang Chinese Medical University, No. 548, Binwen Rd, Hangzhou 310053, China
Center for Vaccine Impact Assessment, Key Laboratory for Quality Monitoring and Evaluation of Vaccines and Biological Prodcuts, National Medical Products Administration, No. 548, Binwen Rd, Hangzhou 310053, China
⁎Corresponding author at: The Institute of Infectious Disease and Vaccine, School of Public Health, Zhejiang Chinese Medical University, No. 548, Binwen Rd, Hangzhou 310053, China.The Institute of Infectious Disease and Vaccine, School of Public Health, Zhejiang Chinese Medical UniversityNo. 548, Binwen RdHangzhou310053China
Highlights |
• | Herpes zoster cases exhibited both seasonal and periodic patterns. |
• | The SARIMA (0,0,2) (1,1,1) ₁₂ - BP (1-2-1) model demonstrated best performance. |
• | This study offering an effective tool for epidemiological forecasting. |
Plan
© 2024
The Author(s). Publié par Elsevier Masson SAS. Tous droits réservés.
Vol 90 - N° 1
Article 106378- janvier 2025 Retour au numéro
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