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Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China.

Rui ZhangZhen GuoYujie MengSongwang WangShaoqiong LiRan NiuYu WangQing GuoYonghong Li
Published in: International journal of environmental research and public health (2021)
Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.
Keyphrases
  • risk factors
  • neural network
  • air pollution
  • physical activity
  • data analysis