ArticleScientific reports2020
Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits.PloS one · 2025Article
- From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases.BioData mining · 2024Article
- Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China.PLoS neglected tropical diseases · 2023Article
- Article
- Co-infection and enterovirus B: post EV-A71 mass vaccination scenario in China.BMC infectious diseases · 2022Article
- Prediction of hand, foot, and mouth disease epidemics in Japan using a long short-term memory approach.PloS one · 2022Article
- Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.Communications biology · 2021Article
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0-3, 3-6 and > 6 years old) from 2011 to 2018 using residual-convolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups.
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