|
Modified SEIQHRDP and machine learning prediction for the epidemics
Li Yike, Elena Gubar St. Petersburg State University, 7/9, Universitetskaya nab., St.Petersburg, 198504, Russia
Аннотация:
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.
Ключевые слова:
dynamics model, Runge-Kutta, ARIMA, Bi-LSTM model.
Образец цитирования:
Li Yike, Elena Gubar, “Modified SEIQHRDP and machine learning prediction for the epidemics”, Contributions to Game Theory and Management, 16 (2023), 110–131
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/cgtm444 https://www.mathnet.ru/rus/cgtm/v16/p110
|
Статистика просмотров: |
Страница аннотации: | 41 | PDF полного текста: | 15 | Список литературы: | 19 |
|