Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2013 May 8

PMID 23650546

Citations 53

Authors

Xingyu Zhang

Yuanyuan Liu

Min Yang

Tao Zhang

Alistair A Young

Xiaosong Li

Affiliations

Soon will be listed here.

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.

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References

Li Q, Guo N, Han Z, Zhang Y, Qi S, Xu Y . Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am J Trop Med Hyg. 2012; 87(2):364-70. PMC: 3414578. DOI: 10.4269/ajtmh.2012.11-0472. View

Liu Q, Liu X, Jiang B, Yang W . Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infect Dis. 2011; 11:218. PMC: 3169483. DOI: 10.1186/1471-2334-11-218. View

Portegys T . A maze learning comparison of Elman, long short-term memory, and Mona neural networks. Neural Netw. 2009; 23(2):306-13. DOI: 10.1016/j.neunet.2009.11.002. View

Hikosaka O, Nakahara H, Rand M, Sakai K, Lu X, Nakamura K . Parallel neural networks for learning sequential procedures. Trends Neurosci. 1999; 22(10):464-71. DOI: 10.1016/s0166-2236(99)01439-3. View

Weisent J, Seaver W, Odoi A, Rohrbach B . Comparison of three time-series models for predicting campylobacteriosis risk. Epidemiol Infect. 2010; 138(6):898-906. DOI: 10.1017/S0950268810000154. View

Chadwick D, Arch B, Wilder-Smith A, Paton N . Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: application of logistic regression analysis. J Clin Virol. 2005; 35(2):147-53. DOI: 10.1016/j.jcv.2005.06.002. View

Rios M, Garcia J, Cubedo M, Perez D . [Time series in the epidemiology of typhoid fever in Spain]. Med Clin (Barc). 1996; 106(18):686-9. View

Nobre F, MONTEIRO A, Telles P, Williamson G . Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology. Stat Med. 2001; 20(20):3051-69. DOI: 10.1002/sim.963. View

Cooke F, Day M, Wain J, Ward L, Threlfall E . Cases of typhoid fever imported into England, Scotland and Wales (2000-2003). Trans R Soc Trop Med Hyg. 2006; 101(4):398-404. DOI: 10.1016/j.trstmh.2006.07.005. View

10.

Sherstinsky A, Picard R . On the efficiency of the orthogonal least squares training method for radial basis function networks. IEEE Trans Neural Netw. 1996; 7(1):195-200. DOI: 10.1109/72.478404. View

11.

Naheed A, Ram P, Brooks W, Hossain M, Parsons M, Talukder K . Burden of typhoid and paratyphoid fever in a densely populated urban community, Dhaka, Bangladesh. Int J Infect Dis. 2010; 14 Suppl 3:e93-9. DOI: 10.1016/j.ijid.2009.11.023. View

12.

Luz P, Mendes B, Codeco C, Struchiner C, Galvani A . Time series analysis of dengue incidence in Rio de Janeiro, Brazil. Am J Trop Med Hyg. 2008; 79(6):933-9. View

13.

Spaeder M, Fackler J . A multi-tiered time-series modelling approach to forecasting respiratory syncytial virus incidence at the local level. Epidemiol Infect. 2011; 140(4):602-7. DOI: 10.1017/S0950268811001026. View

14.

Guan P, Huang D, Zhou B . Forecasting model for the incidence of hepatitis A based on artificial neural network. World J Gastroenterol. 2004; 10(24):3579-82. PMC: 4611996. DOI: 10.3748/wjg.v10.i24.3579. View

15.

CVJETANOVIC B, GRAB B, Uemura K . Epidemiological model of typhoid fever and its use in the planning and evaluation of antityphoid immunization and sanitation programmes. Bull World Health Organ. 1971; 45(1):53-75. PMC: 2427891. View

16.

Wongkoon S, Jaroensutasinee M, Jaroensutasinee K . Development of temporal modeling for prediction of dengue infection in northeastern Thailand. Asian Pac J Trop Med. 2012; 5(3):249-52. DOI: 10.1016/S1995-7645(12)60034-0. View

17.

Helfenstein U . Box-Jenkins modelling of some viral infectious diseases. Stat Med. 1986; 5(1):37-47. DOI: 10.1002/sim.4780050107. View

18.

Ngo L, Tager I, Hadley D . Application of exponential smoothing for nosocomial infection surveillance. Am J Epidemiol. 1996; 143(6):637-47. DOI: 10.1093/oxfordjournals.aje.a008794. View

19.

Bianchini M, Frasconi P, Gori M . Learning without local minima in radial basis function networks. IEEE Trans Neural Netw. 1995; 6(3):749-56. DOI: 10.1109/72.377979. View

20.

Rios M, Garcia J, Sanchez J, Perez D . A statistical analysis of the seasonality in pulmonary tuberculosis. Eur J Epidemiol. 2000; 16(5):483-8. DOI: 10.1023/a:1007653329972. View