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A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China.
PLoS One. 2014; 9(8):e104875.Plos

Abstract

BACKGROUNDS/OBJECTIVE

Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.

METHODS

A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.

RESULTS

The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869 × 10(-4), 0.0029, 0.0419 with a corresponding testing error of 0.9375 × 10(-4), 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.

CONCLUSION

The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.

Authors+Show Affiliations

Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

25119882

Citation

Zhou, Lingling, et al. "A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China." PloS One, vol. 9, no. 8, 2014, pp. e104875.
Zhou L, Yu L, Wang Y, et al. A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. PLoS ONE. 2014;9(8):e104875.
Zhou, L., Yu, L., Wang, Y., Lu, Z., Tian, L., Tan, L., Shi, Y., Nie, S., & Liu, L. (2014). A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. PloS One, 9(8), e104875. https://doi.org/10.1371/journal.pone.0104875
Zhou L, et al. A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China. PLoS ONE. 2014;9(8):e104875. PubMed PMID: 25119882.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. AU - Zhou,Lingling, AU - Yu,Lijing, AU - Wang,Ying, AU - Lu,Zhouqin, AU - Tian,Lihong, AU - Tan,Li, AU - Shi,Yun, AU - Nie,Shaofa, AU - Liu,Li, Y1 - 2014/08/13/ PY - 2014/04/26/received PY - 2014/07/16/accepted PY - 2014/8/15/entrez PY - 2014/8/15/pubmed PY - 2015/5/12/medline SP - e104875 EP - e104875 JF - PloS one JO - PLoS ONE VL - 9 IS - 8 N2 - BACKGROUNDS/OBJECTIVE: Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas. METHODS: A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model. RESULTS: The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869 × 10(-4), 0.0029, 0.0419 with a corresponding testing error of 0.9375 × 10(-4), 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend. CONCLUSION: The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/25119882/A_hybrid_model_for_predicting_the_prevalence_of_schistosomiasis_in_humans_of_Qianjiang_City_China_ L2 - http://dx.plos.org/10.1371/journal.pone.0104875 DB - PRIME DP - Unbound Medicine ER -