Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018.

Scientific Reports
Yuejiao WangQuanyi Wang

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.

References

Oct 30, 2010·Emerging Infectious Diseases·Xiaoli WangQuanyi Wang
Jan 13, 2016·Scientific Reports·Weihua DongQuanyi Wang
Jun 5, 2017·Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases·Nital N GanorkarVaranasi Gopalkrishna
Jun 16, 2018·Influenza and Other Respiratory Viruses·Yi ZhangC Raina MacIntyre
Aug 15, 2018·BMC Infectious Diseases·Yang ZhaoKwok Leung Tsui
Jan 17, 2019·Proceedings of the National Academy of Sciences of the United States of America·Nicholas G ReichJeffrey Shaman
Feb 10, 2019·Proceedings of the National Academy of Sciences of the United States of America·Cécile Viboud, Alessandro Vespignani

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Software Mentioned

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CNNRNN
Res
Pytorch
GAR
VAR

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