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A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China
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 an...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131990/ https://www.ncbi.nlm.nih.gov/pubmed/25119882 http://dx.doi.org/10.1371/journal.pone.0104875 |
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author | Zhou, Lingling Yu, Lijing Wang, Ying Lu, Zhouqin Tian, Lihong Tan, Li Shi, Yun Nie, Shaofa Liu, Li |
author_facet | Zhou, Lingling Yu, Lijing Wang, Ying Lu, Zhouqin Tian, Lihong Tan, Li Shi, Yun Nie, Shaofa Liu, Li |
author_sort | Zhou, Lingling |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4131990 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-41319902014-08-19 A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China Zhou, Lingling Yu, Lijing Wang, Ying Lu, Zhouqin Tian, Lihong Tan, Li Shi, Yun Nie, Shaofa Liu, Li PLoS One Research Article 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. Public Library of Science 2014-08-13 /pmc/articles/PMC4131990/ /pubmed/25119882 http://dx.doi.org/10.1371/journal.pone.0104875 Text en © 2014 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zhou, Lingling Yu, Lijing Wang, Ying Lu, Zhouqin Tian, Lihong Tan, Li Shi, Yun Nie, Shaofa Liu, Li A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China |
title | A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China |
title_full | A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China |
title_fullStr | A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China |
title_full_unstemmed | A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China |
title_short | A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China |
title_sort | hybrid model for predicting the prevalence of schistosomiasis in humans of qianjiang city, china |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131990/ https://www.ncbi.nlm.nih.gov/pubmed/25119882 http://dx.doi.org/10.1371/journal.pone.0104875 |
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