Geodesic
Modified SEIQHRDP and machine learning prediction for the epidemics
Contributions to game theory and management, Tome 16 (2023), pp. 110-131

Voir la notice de l'article provenant de la source Math-Net.Ru

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 ($R_0$) 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.
Keywords: dynamics model, Runge-Kutta, ARIMA, Bi-LSTM model. 
@article{CGTM_2023_16_a8,
     author = {Li Yike and Elena Gubar},
     title = {Modified {SEIQHRDP} and machine learning prediction for the epidemics},
     journal = {Contributions to game theory and management},
     pages = {110--131},
     publisher = {mathdoc},
     volume = {16},
     year = {2023},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/CGTM_2023_16_a8/}
}
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Li Yike; Elena Gubar. Modified SEIQHRDP and machine learning prediction for the epidemics. Contributions to game theory and management, Tome 16 (2023), pp. 110-131. http://geodesic.mathdoc.fr/item/CGTM_2023_16_a8/