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. Rashidi and F. Vafaee},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.12270}
}
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… Expand
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References

SHOWING 1-10 OF 33 REFERENCES
Dynamic causal modelling of COVID-19.
  • 36
  • PDF
Inferring COVID-19 spreading rates and potential change points for case number forecasts
  • 47
  • PDF
The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.
  • 94
  • PDF
Simulation-based Estimation of the Spread of COVID-19 in Iran
  • 19
Early dynamics of transmission and control of COVID-19: a mathematical modelling study
  • 1,258
  • Highly Influential
  • PDF
Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)
  • 1,914
  • PDF
Bi-stability of SUDR+K model of epidemics and test kits applied to COVID-19
  • 17
  • PDF
How will country-based mitigation measures influence the course of the COVID-19 epidemic?
  • 1,398
  • PDF
Temporal Changes in Ebola Transmission in Sierra Leone and Implications for Control Requirements: a Real-time Modelling Study
  • 109
  • PDF
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