title: Comparative study of four time series methods in forecasting typhoid fever incidence in China.
abstract: Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.
authors: Department of Medical Statistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, PR China.
authors: Xingyu
authors: X
authors: Zhang
authors: Yuanyuan
authors: Y
authors: Liu
authors: Min
authors: M
authors: Yang
authors: Tao
authors: T
authors: Zhang
authors: Alistair A
authors: AA
authors: Young
authors: Xiaosong
authors: X
authors: Li
mesh_terms: D000465:Algorithms; D002681:China / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D005544:Forecasting / Q000379:methods; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D016571:Neural Networks, Computer; D014435:Typhoid Fever / Q000453:epidemiology*
references: Am J Trop Med Hyg. 2012 Aug;87(2):364-70
references: 22855772
references: BMC Infect Dis. 2011 Aug 15;11:218
references: 21838933
references: Neural Netw. 2010 Mar;23(2):306-13
references: 19945822
references: Trends Neurosci. 1999 Oct;22(10):464-71
references: 10481194
references: Epidemiol Infect. 2010 Jun;138(6):898-906
references: 20092672
references: J Clin Virol. 2006 Feb;35(2):147-53
references: 16055371
references: Med Clin (Barc). 1996 May 11;106(18):686-9
references: 8801370
references: Stat Med. 2001 Oct 30;20(20):3051-69
references: 11590632
references: Trans R Soc Trop Med Hyg. 2007 Apr;101(4):398-404
references: 17014877
references: IEEE Trans Neural Netw. 1996;7(1):195-200
references: 18255570
references: Int J Infect Dis. 2010 Sep;14 Suppl 3:e93-9
references: 20236850
references: Am J Trop Med Hyg. 2008 Dec;79(6):933-9
references: 19052308
references: Epidemiol Infect. 2012 Apr;140(4):602-7
references: 21676348
references: World J Gastroenterol. 2004 Dec 15;10(24):3579-82
references: 15534910
references: Bull World Health Organ. 1971;45(1):53-75
references: 5316853
references: Asian Pac J Trop Med. 2012 Mar;5(3):249-52
references: 22305794
references: Stat Med. 1986 Jan-Feb;5(1):37-47
references: 3961314
references: Am J Epidemiol. 1996 Mar 15;143(6):637-47
references: 8610681
references: IEEE Trans Neural Netw. 1995;6(3):749-56
references: 18263359
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
references: Ann Epidemiol. 2007 Sep;17(9):654-62
references: 17555986