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Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China.
Sci Rep. 2019 05 29; 9(1):8046.SR

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.

Authors+Show Affiliations

Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China.Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, P.R. China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China. yuanjx@ncst.edu.cn.

Pub Type(s)

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

31142826

Citation

Wang, Yongbin, et al. "Development and Evaluation of a Deep Learning Approach for Modeling Seasonality and Trends in Hand-foot-mouth Disease Incidence in Mainland China." Scientific Reports, vol. 9, no. 1, 2019, p. 8046.
Wang Y, Xu C, Zhang S, et al. Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Sci Rep. 2019;9(1):8046.
Wang, Y., Xu, C., Zhang, S., Yang, L., Wang, Z., Zhu, Y., & Yuan, J. (2019). Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Scientific Reports, 9(1), 8046. https://doi.org/10.1038/s41598-019-44469-9
Wang Y, et al. Development and Evaluation of a Deep Learning Approach for Modeling Seasonality and Trends in Hand-foot-mouth Disease Incidence in Mainland China. Sci Rep. 2019 05 29;9(1):8046. PubMed PMID: 31142826.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. AU - Wang,Yongbin, AU - Xu,Chunjie, AU - Zhang,Shengkui, AU - Yang,Li, AU - Wang,Zhende, AU - Zhu,Ying, AU - Yuan,Juxiang, Y1 - 2019/05/29/ PY - 2018/08/07/received PY - 2019/03/06/accepted PY - 2019/5/31/entrez PY - 2019/5/31/pubmed PY - 2020/10/21/medline SP - 8046 EP - 8046 JF - Scientific reports JO - Sci Rep VL - 9 IS - 1 N2 - 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. SN - 2045-2322 UR - https://www.unboundmedicine.com/medline/citation/31142826/Development_and_evaluation_of_a_deep_learning_approach_for_modeling_seasonality_and_trends_in_hand_foot_mouth_disease_incidence_in_mainland_China_ L2 - https://doi.org/10.1038/s41598-019-44469-9 DB - PRIME DP - Unbound Medicine ER -