|
Computational mathematics
On mathematical models of COVID-19 pandemic
O. I. Krivorotko, S. I. Kabanikhin Sobolev Institute of Mathematics, pr. Koptyuga, 4, 630090, Novosibirsk, Russia
Abstract:
The mathematical models for analysis and forecasting of COVID-19 pandemic based on time-series models, differential equations (SIR models based on odinary, partial and stochastic differential equations), agent-based models, mean field games and its combinations are considered. Inverse problems for mathematical models in epidemiology of COVID-19 are formulated in the variational form. The numerical results of modeling and scenarios of COVID-19 propagation in Novosibirsk region are demonstrated and discussed. The epidemiology parameters of COVID-19 propagation in Novosibirsk region (contagiosity, hospitalization and mortality rates, asymptomatic cases) are identified. The combination of differential and agent-based models increases the quality of forecast scenarios.
Keywords:
epidemiology, COVID-19, time-series models, SIR, agent-based models, mean field games, inverse problems, forecasting.
Received December 12, 2022, published November 21, 2023
Citation:
O. I. Krivorotko, S. I. Kabanikhin, “On mathematical models of COVID-19 pandemic”, Sib. Èlektron. Mat. Izv., 20:2 (2023), 1211–1268
Linking options:
https://www.mathnet.ru/eng/semr1638 https://www.mathnet.ru/eng/semr/v20/i2/p1211
|
|