Corpus ID: 218889567

Forecasting the Spread of Covid-19 Under Control Scenarios Using LSTM and Dynamic Behavioral Models

@article{Zandavi2020ForecastingTS,
  title={Forecasting the Spread of Covid-19 Under Control Scenarios Using LSTM and Dynamic Behavioral Models},
  author={S. M. Zandavi and T. H. Rashidi and Fatemeh Vafaee},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.12270}
}

S. M. Zandavi, T. H. Rashidi, Fatemeh Vafaee

Published 2020

Computer Science, Physics

ArXiv

To accurately predict the regional spread of Covid-19 infection, this study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arisen from confounding variables underlying the spread of the Covid-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in… CONTINUE READING

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