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Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea

Hand–foot–mouth disease (HFMD) is a viral disease that occurs primarily in children. Meteorological factors have a significant impact on its popularity annually in Korea. This study proposes a new HFMD prediction model using a dual-attention-based recurrent neural network (DA-RNN) and important weat...

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Autores principales: Lee, Sieun, Kim, Sangil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547784/
https://www.ncbi.nlm.nih.gov/pubmed/37789071
http://dx.doi.org/10.1038/s41598-023-43881-6
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author Lee, Sieun
Kim, Sangil
author_facet Lee, Sieun
Kim, Sangil
author_sort Lee, Sieun
collection PubMed
description Hand–foot–mouth disease (HFMD) is a viral disease that occurs primarily in children. Meteorological factors have a significant impact on its popularity annually in Korea. This study proposes a new HFMD prediction model using a dual-attention-based recurrent neural network (DA-RNN) and important weather factors for HFMD in Korea. First, suspected cases of HFMD in each state were predicted using meteorological factors from the DA-RNN. Second, the weather factors were divided into six categories: temperature, wind, rainfall, day length, humidity, and air pollution to conduct sensitivity analysis. Because of this prediction, the proposed model showed the best performance in predicting the number of suspected HFMD cases in a week compared with other RNN methods. Sensitivity analysis showed that air pollution and rainfall play an important role in HFMD in Korea. This model provides information for HFMD prevention and control and can be extended to predict other infectious diseases.
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spelling pubmed-105477842023-10-05 Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea Lee, Sieun Kim, Sangil Sci Rep Article Hand–foot–mouth disease (HFMD) is a viral disease that occurs primarily in children. Meteorological factors have a significant impact on its popularity annually in Korea. This study proposes a new HFMD prediction model using a dual-attention-based recurrent neural network (DA-RNN) and important weather factors for HFMD in Korea. First, suspected cases of HFMD in each state were predicted using meteorological factors from the DA-RNN. Second, the weather factors were divided into six categories: temperature, wind, rainfall, day length, humidity, and air pollution to conduct sensitivity analysis. Because of this prediction, the proposed model showed the best performance in predicting the number of suspected HFMD cases in a week compared with other RNN methods. Sensitivity analysis showed that air pollution and rainfall play an important role in HFMD in Korea. This model provides information for HFMD prevention and control and can be extended to predict other infectious diseases. Nature Publishing Group UK 2023-10-03 /pmc/articles/PMC10547784/ /pubmed/37789071 http://dx.doi.org/10.1038/s41598-023-43881-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lee, Sieun
Kim, Sangil
Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea
title Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea
title_full Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea
title_fullStr Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea
title_full_unstemmed Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea
title_short Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea
title_sort dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in korea
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547784/
https://www.ncbi.nlm.nih.gov/pubmed/37789071
http://dx.doi.org/10.1038/s41598-023-43881-6
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