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
    4 Citations

    References

    SHOWING 1-10 OF 33 REFERENCES
    Dynamic causal modelling of COVID-19.
    • 33
    • PDF
    Inferring COVID-19 spreading rates and potential change points for case number forecasts
    • 43
    • PDF
    The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.
    • 85
    • PDF
    Simulation-based Estimation of the Spread of COVID-19 in Iran
    • 16
    • PDF
    Early dynamics of transmission and control of COVID-19: a mathematical modelling study
    • 1,158
    • Highly Influential
    • PDF
    Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)
    • 1,740
    • PDF
    Bi-stability of SUDR+K model of epidemics and test kits applied to COVID-19
    • 16
    • PDF
    Temporal Changes in Ebola Transmission in Sierra Leone and Implications for Control Requirements: a Real-time Modelling Study
    • 109
    • PDF