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Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.
Int J Environ Res Public Health. 2016 Mar 23; 13(4):355.IJ

Abstract

BACKGROUND

We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model.

METHODS

We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016.

RESULTS

The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase.

CONCLUSIONS

The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis.

Authors+Show Affiliations

Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. zllgwy@126.com. Department of Information, Research Institute of Field Surgery, Daping Hospital of Third Military Medical University, Chongqing 400042, China. zllgwy@126.com.Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China. xiaj0608@163.com.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. yulijing321@outlook.com.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. wangying@whu.edu.cn.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. shiyuntj@hust.edu.cn.Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China. shunxiangcai@163.com.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. sf_nie@mails.tjmu.edu.cn.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27023573

Citation

Zhou, Lingling, et al. "Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans." International Journal of Environmental Research and Public Health, vol. 13, no. 4, 2016, p. 355.
Zhou L, Xia J, Yu L, et al. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. Int J Environ Res Public Health. 2016;13(4):355.
Zhou, L., Xia, J., Yu, L., Wang, Y., Shi, Y., Cai, S., & Nie, S. (2016). Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. International Journal of Environmental Research and Public Health, 13(4), 355. https://doi.org/10.3390/ijerph13040355
Zhou L, et al. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. Int J Environ Res Public Health. 2016 Mar 23;13(4):355. PubMed PMID: 27023573.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. AU - Zhou,Lingling, AU - Xia,Jing, AU - Yu,Lijing, AU - Wang,Ying, AU - Shi,Yun, AU - Cai,Shunxiang, AU - Nie,Shaofa, Y1 - 2016/03/23/ PY - 2015/12/02/received PY - 2016/01/25/revised PY - 2016/02/29/accepted PY - 2016/3/30/entrez PY - 2016/3/31/pubmed PY - 2016/12/15/medline KW - ARIMA model KW - NARNN model KW - forecasting KW - hybrid model KW - schistosomiasis SP - 355 EP - 355 JF - International journal of environmental research and public health JO - Int J Environ Res Public Health VL - 13 IS - 4 N2 - BACKGROUND: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. METHODS: We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. RESULTS: The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. CONCLUSIONS: The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis. SN - 1660-4601 UR - https://www.unboundmedicine.com/medline/citation/27023573/Using_a_Hybrid_Model_to_Forecast_the_Prevalence_of_Schistosomiasis_in_Humans_ L2 - http://www.mdpi.com/resolver?pii=ijerph13040355 DB - PRIME DP - Unbound Medicine ER -