title: A hybrid seasonal prediction model for tuberculosis incidence in China.
abstract: BACKGROUND
  Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China.
  
  
  METHODS
  Data of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Health, China. A seasonal autoregressive integrated moving average (SARIMA) model and a hybrid model which combined the SARIMA model and a generalized regression neural network model were used to fit the data from 2005 to 2010. Simulation performance parameters of mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the goodness-of-fit between these two models. Data from 2011 TB incidence data was used to validate the chosen model.
  
  
  RESULTS
  Although both two models could reasonably forecast the incidence of TB, the hybrid model demonstrated better goodness-of-fit than the SARIMA model. For the hybrid model, the MSE, MAE and MAPE were 38969150, 3406.593 and 0.030, respectively. For the SARIMA model, the corresponding figures were 161835310, 8781.971 and 0.076, respectively. The seasonal trend of TB incidence is predicted to have lower monthly incidence in January and February and higher incidence from March to June.
  
  
  CONCLUSIONS
  The hybrid model showed better TB incidence forecasting than the SARIMA model. There is an obvious seasonal trend of TB incidence in China that differed from other countries.
authors: School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.
authors: Shiyi
authors: S
authors: Cao
authors: Feng
authors: F
authors: Wang
authors: Wilson
authors: W
authors: Tam
authors: Lap Ah
authors: LA
authors: Tse
authors: Jean Hee
authors: JH
authors: Kim
authors: Junan
authors: J
authors: Liu
authors: Zuxun
authors: Z
authors: Lu
mesh_terms: D002681:China / Q000453:epidemiology; D003198:Computer Simulation; D018563:Disease Notification; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D012621:Seasons; D018511:Systems Integration; D014376:Tuberculosis / Q000175:diagnosis / Q000453:epidemiology* / Q000401:mortality
references: Rev Esp Salud Publica. 2009 Sep-Oct;83(5):751-7
references: 20111823
references: Lancet. 2006 Mar 18;367(9514):926-37
references: 16546541
references: Lancet. 2006 Mar 18;367(9514):938-40
references: 16546542
references: Zhonghua Liu Xing Bing Xue Za Zhi. 2009 Sep;30(9):964-8
references: 20193238
references: J Glob Infect Dis. 2011 Jan;3(1):46-55
references: 21572609
references: Emerg Infect Dis. 2000 Mar-Apr;6(2):148-57
references: 10756148
references: Rev Soc Bras Med Trop. 2010 Sep-Oct;43(5):567-70
references: 21085871
references: Lancet. 2004 Oct 30-Nov 5;364(9445):1613-4
references: 15519633
references: Int J Epidemiol. 2005 Aug;34(4):924-30
references: 15851395
references: Eur Respir J. 2009 Oct;34(4):921-5
references: 19386690
references: Zhongguo Ji Sheng Chong Xue Yu Ji Sheng Chong Bing Za Zhi. 2007 Jun;25(3):232-6
references: 18038786
references: Chest. 1999 Aug;116(2):409-15
references: 10453870
references: Eur J Epidemiol. 2000 May;16(5):483-8
references: 10997837
references: Jpn J Infect Dis. 2010 Jul;63(4):264-70
references: 20657066