Cargando…

Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China

BACKGROUND: Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. Ho...

Descripción completa

Detalles Bibliográficos
Autores principales: Geng, Xiaoran, Ma, Yue, Cai, Wennian, Zha, Yuanyi, Zhang, Tao, Zhang, Huadong, Yang, Changhong, Yin, Fei, Shui, Tiejun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511093/
https://www.ncbi.nlm.nih.gov/pubmed/37683009
http://dx.doi.org/10.1371/journal.pntd.0011587
_version_ 1785108072827453440
author Geng, Xiaoran
Ma, Yue
Cai, Wennian
Zha, Yuanyi
Zhang, Tao
Zhang, Huadong
Yang, Changhong
Yin, Fei
Shui, Tiejun
author_facet Geng, Xiaoran
Ma, Yue
Cai, Wennian
Zha, Yuanyi
Zhang, Tao
Zhang, Huadong
Yang, Changhong
Yin, Fei
Shui, Tiejun
author_sort Geng, Xiaoran
collection PubMed
description BACKGROUND: Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. METHODS: We collected the daily number of HFMD cases among children aged 0–14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks. RESULTS: From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM(10). The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay. CONCLUSIONS: The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.
format Online
Article
Text
id pubmed-10511093
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-105110932023-09-21 Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China Geng, Xiaoran Ma, Yue Cai, Wennian Zha, Yuanyi Zhang, Tao Zhang, Huadong Yang, Changhong Yin, Fei Shui, Tiejun PLoS Negl Trop Dis Research Article BACKGROUND: Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. METHODS: We collected the daily number of HFMD cases among children aged 0–14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks. RESULTS: From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM(10). The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay. CONCLUSIONS: The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors. Public Library of Science 2023-09-08 /pmc/articles/PMC10511093/ /pubmed/37683009 http://dx.doi.org/10.1371/journal.pntd.0011587 Text en © 2023 Geng et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Geng, Xiaoran
Ma, Yue
Cai, Wennian
Zha, Yuanyi
Zhang, Tao
Zhang, Huadong
Yang, Changhong
Yin, Fei
Shui, Tiejun
Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China
title Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China
title_full Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China
title_fullStr Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China
title_full_unstemmed Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China
title_short Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China
title_sort evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: a case study of chengdu, china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511093/
https://www.ncbi.nlm.nih.gov/pubmed/37683009
http://dx.doi.org/10.1371/journal.pntd.0011587
work_keys_str_mv AT gengxiaoran evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT mayue evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT caiwennian evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT zhayuanyi evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT zhangtao evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT zhanghuadong evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT yangchanghong evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT yinfei evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina
AT shuitiejun evaluationofmodelsformultistepforecastingofhandfootandmouthdiseaseusingmultiinputmultioutputacasestudyofchengduchina