ArticleScientific reports2019
Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004-2027).Journal of global health · 2026Article
- An ARIMAX model integrating Baidu index for improved hand, foot, and mouth disease incidence prediction.Scientific reports · 2026Article
- Epidemiological characteristics and incidence prediction of varicella from 2014 to 2023 in Chongqing, China.Frontiers in public health · 2026Article
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- A comparative study of time series foundation models for hand, foot, and mouth disease forecasting: TimesFM, Moirai, and traditional approaches.Frontiers in public health · 2025Article
- Analysis and forecasting of syphilis trends in mainland China based on hybrid time series models.Epidemiology and infection · 2024Article
- Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model.BMC infectious diseases · 2024Article
- Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China.Epidemiology and infection · 2023Article
- Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea.Scientific reports · 2023Article
- Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.BMC infectious diseases · 2023Article
- Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.Epidemiology and infection · 2023Article
- Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.Environmental science and pollution research international · 2023Article
- Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.PloS one · 2023Article
- Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm.BMC public health · 2022Article
- Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model.Epidemiology and infection · 2022Article
- Prediction of hand, foot, and mouth disease epidemics in Japan using a long short-term memory approach.PloS one · 2022Article
- Deep learning time series prediction models in surveillance data of hepatitis incidence in China.PloS one · 2022Article
- Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis.Frontiers in public health · 2022Article
- The fluid management and hemodynamic characteristics of PiCCO employed on young children with severe hand, foot, and mouth disease-a retrospective study.BMC infectious diseases · 2021Article
- Forecasting the incidence of mumps in Chongqing based on a SARIMA model.BMC public health · 2021Article
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7 authors.
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Abstract
The high incidence, seasonal pattern and frequent outbreaks of hand, foot, and mouth disease (HFMD) represent a threat for millions of children in mainland China. And advanced response is being used to address this. Here, we aimed to model time series with a long short-term memory (LSTM) based on the HFMD notified data from June 2008 to June 2018 and the ultimate performance was compared with the autoregressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NAR). The results indicated that the identified best-fitting LSTM with the better superiority, be it in modeling dataset or two robustness tests dataset, than the best-conducting NAR and seasonal ARIMA (SARIMA) methods in forecasting performances, including the minimum indices of root mean square error, mean absolute error and mean absolute percentage error. The epidemic trends of HFMD remained stable during the study period, but the reported cases were even at significantly high levels with a notable high-risk seasonality in summer, and the incident cases projected by the LSTM would still be fairly high with a slightly upward trend in the future. In this regard, the LSTM approach should be highlighted in forecasting the epidemics of HFMD, and therefore assisting decision makers in making efficient decisions derived from the early detection of the disease incidents.
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