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A hybrid model for short-term bacillary dysentery prediction in Yichang City, China.
Jpn J Infect Dis. 2010 Jul; 63(4):264-70.JJ

Abstract

Bacillary dysentery is still a common and serious public health problem in China. This paper is aimed at developing and evaluating an innovative hybrid model, which combines the seasonal autoregressive integrated moving average (SARIMA) and the generalized regression neural network (GRNN) models, for bacillary dysentery forecasting. Data of monthly bacillary dysentery incidence in Yichang City from 2000-2007 was obtained from Yichang Disease Control and Prevention Center. The SARIMA and SARIMA-GRNN model were developed and validated by dividing the data file into two data sets: data from the past 5 years was used to construct the models, and data from January to June of the 6th year was used to validate them. Simulation and forecasting performance was evaluated and compared between the two models. The hybrid SARIMA-GRNN model was found to outperform the SARIMA model with the lower mean square error, mean absolute error, and mean absolute percentage error in simulation and prediction results. Developing and applying the SARIMA-GRNN hybrid model is an effective decision supportive method for producing reliable forecasts of bacillary dysentery for the study area.

Authors+Show Affiliations

Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China. weirongy@gmail.comNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Evaluation Study
Journal Article

Language

eng

PubMed ID

20657066

Citation

Yan, Weirong, et al. "A Hybrid Model for Short-term Bacillary Dysentery Prediction in Yichang City, China." Japanese Journal of Infectious Diseases, vol. 63, no. 4, 2010, pp. 264-70.
Yan W, Xu Y, Yang X, et al. A hybrid model for short-term bacillary dysentery prediction in Yichang City, China. Jpn J Infect Dis. 2010;63(4):264-70.
Yan, W., Xu, Y., Yang, X., & Zhou, Y. (2010). A hybrid model for short-term bacillary dysentery prediction in Yichang City, China. Japanese Journal of Infectious Diseases, 63(4), 264-70.
Yan W, et al. A Hybrid Model for Short-term Bacillary Dysentery Prediction in Yichang City, China. Jpn J Infect Dis. 2010;63(4):264-70. PubMed PMID: 20657066.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A hybrid model for short-term bacillary dysentery prediction in Yichang City, China. AU - Yan,Weirong, AU - Xu,Yong, AU - Yang,Xiaobing, AU - Zhou,Yikai, PY - 2010/7/27/entrez PY - 2010/7/27/pubmed PY - 2010/11/3/medline SP - 264 EP - 70 JF - Japanese journal of infectious diseases JO - Jpn. J. Infect. Dis. VL - 63 IS - 4 N2 - Bacillary dysentery is still a common and serious public health problem in China. This paper is aimed at developing and evaluating an innovative hybrid model, which combines the seasonal autoregressive integrated moving average (SARIMA) and the generalized regression neural network (GRNN) models, for bacillary dysentery forecasting. Data of monthly bacillary dysentery incidence in Yichang City from 2000-2007 was obtained from Yichang Disease Control and Prevention Center. The SARIMA and SARIMA-GRNN model were developed and validated by dividing the data file into two data sets: data from the past 5 years was used to construct the models, and data from January to June of the 6th year was used to validate them. Simulation and forecasting performance was evaluated and compared between the two models. The hybrid SARIMA-GRNN model was found to outperform the SARIMA model with the lower mean square error, mean absolute error, and mean absolute percentage error in simulation and prediction results. Developing and applying the SARIMA-GRNN hybrid model is an effective decision supportive method for producing reliable forecasts of bacillary dysentery for the study area. SN - 1884-2836 UR - https://www.unboundmedicine.com/medline/citation/20657066/A_hybrid_model_for_short_term_bacillary_dysentery_prediction_in_Yichang_City_China_ L2 - http://www0.nih.go.jp/JJID/63/264.html DB - PRIME DP - Unbound Medicine ER -