title: Modified SEIQHRDP and Machine Learning Prediction for the Epidemics
metadata.name: dc.contributor.author
metadata.value: Li, Yike
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Gubar, Elena
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-02-26T17:42:06Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-02-26T17:42:06Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023
metadata.lang: -
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu31.2023.08
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/44950
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: This paper is dedicated to investigating the transmission and
  prediction of viruses within human society. In the first phase, we augment
  the classical Susceptible-Exposed-Infectious-Recovered (SEIR) model by incorporating
  four novel states: protected status (P), quarantine status (Q),
  self-home status (H), and death status (D). The numerical solution of this
  extended model is obtained using the well-established fourth-order Runge-
  Kutta algorithm. Subsequently, we employ the next matrix method to calculate
  the basic reproduction number (R0) of the infectious disease model.
  We substantiate the stability of the basic reproductive number through an
  analysis grounded in Routh-Hurwitz theory. Lastly, we turn to the application
  and comparison of statistical models, specifically the Autoregressive
  Integrated Moving Average (ARIMA) and Bidirectional Long Short-Term
  Memory (Bi-LSTM) models, for time series prediction.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Contributions to Game Theory and Management;Volume 16
metadata.lang: -
metadata.name: dc.subject
metadata.value: dynamics model
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Runge-Kutta
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: ARIMA
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Bi-LSTM model
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Modified SEIQHRDP and Machine Learning Prediction for the Epidemics
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 110-131.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/44950/1/110-131.pdf
resources.format: Adobe PDF
resources.size: 2,31 MB