Tags

Type your tag names separated by a space and hit enter

Comparative study of four time series methods in forecasting typhoid fever incidence in China.
PLoS One. 2013; 8(5):e63116.Plos

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+Show Affiliations

Department of Medical Statistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, PR China.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23650546

Citation

Zhang, Xingyu, et al. "Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China." PloS One, vol. 8, no. 5, 2013, pp. e63116.
Zhang X, Liu Y, Yang M, et al. Comparative study of four time series methods in forecasting typhoid fever incidence in China. PLoS ONE. 2013;8(5):e63116.
Zhang, X., Liu, Y., Yang, M., Zhang, T., Young, A. A., & Li, X. (2013). Comparative study of four time series methods in forecasting typhoid fever incidence in China. PloS One, 8(5), e63116. https://doi.org/10.1371/journal.pone.0063116
Zhang X, et al. Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China. PLoS ONE. 2013;8(5):e63116. PubMed PMID: 23650546.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparative study of four time series methods in forecasting typhoid fever incidence in China. AU - Zhang,Xingyu, AU - Liu,Yuanyuan, AU - Yang,Min, AU - Zhang,Tao, AU - Young,Alistair A, AU - Li,Xiaosong, Y1 - 2013/05/01/ PY - 2012/07/04/received PY - 2013/04/01/accepted PY - 2013/5/8/entrez PY - 2013/5/8/pubmed PY - 2013/12/16/medline SP - e63116 EP - e63116 JF - PloS one JO - PLoS ONE VL - 8 IS - 5 N2 - 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/23650546/Comparative_study_of_four_time_series_methods_in_forecasting_typhoid_fever_incidence_in_China_ L2 - http://dx.plos.org/10.1371/journal.pone.0063116 DB - PRIME DP - Unbound Medicine ER -