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Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China

Hepatitis E has placed a heavy burden on China, especially in Jiangsu Province, so accurately predicting the incidence of hepatitis E benefits to alleviate the medical burden. In this paper, we propose a new attentive bidirectional long short-term memory network (denoted as BiLSTM–Attention) to pred...

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Autores principales: Wu, Tianxing, Wang, Minghao, Cheng, Xiaoqing, Liu, Wendong, Zhu, Shutong, Zhang, Xuefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574096/
https://www.ncbi.nlm.nih.gov/pubmed/36262244
http://dx.doi.org/10.3389/fpubh.2022.942543
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author Wu, Tianxing
Wang, Minghao
Cheng, Xiaoqing
Liu, Wendong
Zhu, Shutong
Zhang, Xuefeng
author_facet Wu, Tianxing
Wang, Minghao
Cheng, Xiaoqing
Liu, Wendong
Zhu, Shutong
Zhang, Xuefeng
author_sort Wu, Tianxing
collection PubMed
description Hepatitis E has placed a heavy burden on China, especially in Jiangsu Province, so accurately predicting the incidence of hepatitis E benefits to alleviate the medical burden. In this paper, we propose a new attentive bidirectional long short-term memory network (denoted as BiLSTM–Attention) to predict the incidence of hepatitis E for all 13 cities in Jiangsu Province, China. Besides, we also explore the performance of adding meteorological factors and the Baidu (the most widely used Chinese search engine) index as additional training data for the prediction of our BiLSTM–Attention model. SARIMAX, GBDT, LSTM, BiLSTM, and BiLSTM–Attention models are tested in this study, based on the monthly incidence rates of hepatitis E, meteorological factors, and the Baidu index collected from 2011 to 2019 for the 13 cities in Jiangsu province, China. From January 2011 to December 2019, a total of 29,339 cases of hepatitis E were detected in all cities in Jiangsu Province, and the average monthly incidence rate for each city is 0.359 per 100,000 persons. Root mean square error (RMSE) and mean absolute error (MAE) are used for model selection and performance evaluation. The BiLSTM–Attention model considering meteorological factors and the Baidu index has the best performance for hepatitis E prediction in all cities, and it gets at least 10% improvement in RMSE and MAE for all 13 cities in Jiangsu province, which means the model has significantly improved the learning ability, generalizability, and prediction accuracy when comparing with others.
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spelling pubmed-95740962022-10-18 Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China Wu, Tianxing Wang, Minghao Cheng, Xiaoqing Liu, Wendong Zhu, Shutong Zhang, Xuefeng Front Public Health Public Health Hepatitis E has placed a heavy burden on China, especially in Jiangsu Province, so accurately predicting the incidence of hepatitis E benefits to alleviate the medical burden. In this paper, we propose a new attentive bidirectional long short-term memory network (denoted as BiLSTM–Attention) to predict the incidence of hepatitis E for all 13 cities in Jiangsu Province, China. Besides, we also explore the performance of adding meteorological factors and the Baidu (the most widely used Chinese search engine) index as additional training data for the prediction of our BiLSTM–Attention model. SARIMAX, GBDT, LSTM, BiLSTM, and BiLSTM–Attention models are tested in this study, based on the monthly incidence rates of hepatitis E, meteorological factors, and the Baidu index collected from 2011 to 2019 for the 13 cities in Jiangsu province, China. From January 2011 to December 2019, a total of 29,339 cases of hepatitis E were detected in all cities in Jiangsu Province, and the average monthly incidence rate for each city is 0.359 per 100,000 persons. Root mean square error (RMSE) and mean absolute error (MAE) are used for model selection and performance evaluation. The BiLSTM–Attention model considering meteorological factors and the Baidu index has the best performance for hepatitis E prediction in all cities, and it gets at least 10% improvement in RMSE and MAE for all 13 cities in Jiangsu province, which means the model has significantly improved the learning ability, generalizability, and prediction accuracy when comparing with others. Frontiers Media S.A. 2022-10-03 /pmc/articles/PMC9574096/ /pubmed/36262244 http://dx.doi.org/10.3389/fpubh.2022.942543 Text en Copyright © 2022 Wu, Wang, Cheng, Liu, Zhu and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Wu, Tianxing
Wang, Minghao
Cheng, Xiaoqing
Liu, Wendong
Zhu, Shutong
Zhang, Xuefeng
Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China
title Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China
title_full Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China
title_fullStr Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China
title_full_unstemmed Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China
title_short Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China
title_sort predicting incidence of hepatitis e for thirteen cities in jiangsu province, china
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574096/
https://www.ncbi.nlm.nih.gov/pubmed/36262244
http://dx.doi.org/10.3389/fpubh.2022.942543
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