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Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.
PLoS One. 2015; 10(8):e0135492.Plos

Abstract

BACKGROUND

Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS.

METHODS

Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model.

RESULTS

The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve.

CONCLUSION

Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

Authors+Show Affiliations

Department of Epidemiology, School of Public Health, China Medical University, Shenyang, PR China.Liaoning Provincial Center for Disease Control and Prevention, Shenyang, PR China.Liaoning Provincial Center for Disease Control and Prevention, Shenyang, PR China.Department of Epidemiology, School of Public Health, China Medical University, Shenyang, PR China.Department of Epidemiology, School of Public Health, China Medical University, Shenyang, PR China.Department of Epidemiology, School of Public Health, China Medical University, Shenyang, PR China.Department of Epidemiology, School of Public Health, China Medical University, Shenyang, PR China.

Pub Type(s)

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

Language

eng

PubMed ID

26270814

Citation

Wu, Wei, et al. "Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever With Renal Syndrome in Jiangsu Province, China." PloS One, vol. 10, no. 8, 2015, pp. e0135492.
Wu W, Guo J, An S, et al. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China. PLoS ONE. 2015;10(8):e0135492.
Wu, W., Guo, J., An, S., Guan, P., Ren, Y., Xia, L., & Zhou, B. (2015). Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China. PloS One, 10(8), e0135492. https://doi.org/10.1371/journal.pone.0135492
Wu W, et al. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever With Renal Syndrome in Jiangsu Province, China. PLoS ONE. 2015;10(8):e0135492. PubMed PMID: 26270814.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China. AU - Wu,Wei, AU - Guo,Junqiao, AU - An,Shuyi, AU - Guan,Peng, AU - Ren,Yangwu, AU - Xia,Linzi, AU - Zhou,Baosen, Y1 - 2015/08/13/ PY - 2015/02/12/received PY - 2015/07/23/accepted PY - 2015/8/14/entrez PY - 2015/8/14/pubmed PY - 2016/5/18/medline SP - e0135492 EP - e0135492 JF - PloS one JO - PLoS ONE VL - 10 IS - 8 N2 - BACKGROUND: Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. METHODS: Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. RESULTS: The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. CONCLUSION: Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/26270814/Comparison_of_Two_Hybrid_Models_for_Forecasting_the_Incidence_of_Hemorrhagic_Fever_with_Renal_Syndrome_in_Jiangsu_Province_China_ L2 - http://dx.plos.org/10.1371/journal.pone.0135492 DB - PRIME DP - Unbound Medicine ER -