Forecasting the incidence of mumps in Chongqing based on a SARIMA model

被引:22
|
作者
Qiu, Hongfang [1 ]
Zhao, Han [2 ]
Xiang, Haiyan [1 ]
Ou, Rong [3 ]
Yi, Jing [1 ]
Hu, Ling [1 ]
Zhu, Hua [1 ]
Ye, Mengliang [1 ]
机构
[1] Chongqing Med Univ, Sch Publ Hlth & Management, Dept Epidemiol & Hlth Stat, Chongqing 400016, Peoples R China
[2] Chongqing Municipal Ctr Dis Control & Prevent, Chongqing 400042, Peoples R China
[3] Chongqing Med Univ, Dept Med Informat Lib, Chongqing 400016, Peoples R China
关键词
Incidence; Mumps; SARIMA model; Chongqing; TUBERCULOSIS INCIDENCE; CHINA; OUTBREAK; TRENDS; DENGUE; HAND;
D O I
10.1186/s12889-021-10383-x
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
BackgroundMumps is classified as a class C infection disease in China, and the Chongqing area has one of the highest incidence rates in the country. We aimed to establish a prediction model for mumps in Chongqing and analyze its seasonality, which is important for risk analysis and allocation of resources in the health sector.MethodsData on incidence of mumps from January 2004 to December 2018 were obtained from Chongqing Municipal Bureau of Disease Control and Prevention. The incidence of mumps from 2004 to 2017 was fitted using a seasonal autoregressive comprehensive moving average (SARIMA) model. The root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to compare the goodness of fit of the models. The 2018 incidence data were used for validation.ResultsFrom 2004 to 2018, a total of 159,181 cases (93,655 males and 65,526 females) of mumps were reported in Chongqing, with significantly more men than women. The age group of 0-19 years old accounted for 92.41% of all reported cases, and students made up the largest proportion (62.83%), followed by scattered children and children in kindergarten. The SARIMA(2, 1, 1) x (0, 1, 1)(12) was the best fit model, RMSE and MAPE were 0.9950 and 39.8396%, respectively.ConclusionBased on the study findings, the incidence of mumps in Chongqing has an obvious seasonal trend, and SARIMA(2, 1, 1) x (0, 1, 1)(12) model can also predict the incidence of mumps well. The SARIMA model of time series analysis is a feasible and simple method for predicting mumps in Chongqing.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] FUZZY TIME SERIES AND SARIMA MODEL FOR FORECASTING TOURIST ARRIVALS TO BALI
    Elena, Maria
    Lee, Muhammad Hisyam
    Suhartono
    Hossein
    Haizum, Nur
    Bazilah, Nur Arina
    JURNAL TEKNOLOGI, 2012, 57
  • [32] Forecasting on the crude palm oil production in Malaysia using SARIMA Model
    Tayib, Siti Amnah Mohd
    Nor, Siti Rohani Mohd
    Norrulashikin, Siti Mariam
    Journal of Physics: Conference Series, 2021, 1988 (01):
  • [33] Forecasting Dengue Fever Cases in Guangdong Province Using SARIMA Model
    Qian, Jun
    Li, Li
    Liu, Guoqing
    INTERNATIONAL CONFERENCE ON CONTROL SYSTEM AND AUTOMATION (CSA 2013), 2013, : 314 - 320
  • [34] SEASONAL AVERAGE RAINFALL FORECASTING IN TAMIL NADU - BOOTSTRAP SARIMA MODEL
    Selvam, S.
    Bharathidass, S.
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 2023, 19 (02): : 483 - 491
  • [35] Load Forecasting Assessment Using SARIMA Model and Fuzzy Inductive Reasoning
    Gonzalez Cabrera, Nestor
    Gutierrez-Alcaraz, G.
    Gil, Esteban
    2013 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM 2013), 2013, : 561 - 565
  • [36] Mobile Data Traffic Forecasting in UMTS Networks Based on SARIMA Model: The Case of Addis Ababa, Ethiopia
    Medhn, Samuel
    Seifu, Bethelhem
    Salem, Amel
    Hailemariam, Dereje
    2017 IEEE AFRICON, 2017, : 285 - 290
  • [37] INCIDENCE OF MUMPS
    PALMER, SR
    BIFFIN, A
    JOURNAL OF THE ROYAL COLLEGE OF GENERAL PRACTITIONERS, 1989, 39 (318): : 34 - 34
  • [38] An EMD-SARIMA-based modeling approach for air traffic forecasting
    Nai W.
    Liu L.
    Wang S.
    Dong D.
    Algorithms, 2017, 10 (04)
  • [39] Characterization of Low Visibility and Forecasting Model in Chongqing Central Area
    Han, Yu
    Liu, Yi
    Zhang, Yaping
    He, Jun
    Zhang, Yan
    Guo, Qu
    Wang, Huan
    ADVANCES IN METEOROLOGY, 2025, 2025 (01)
  • [40] An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China
    Wang, Yongbin
    Xu, Chunjie
    Li, Yuchun
    Wu, Weidong
    Gui, Lihui
    Ren, Jingchao
    Yao, Sanqiao
    INFECTION AND DRUG RESISTANCE, 2020, 13 : 867 - 880