This article is cited in 5 scientific papers (total in 5 papers)
Mathematical Modeling
Stochastic modeling of dynamics of the spread of COVID-19 infection taking into account the heterogeneity of population according to immunological, clinical and epidemiological criteria
Abstract:
Here we present a stochastic model of the spread of Covid-19 infection in a certain region. The model is a continuous-discrete random process that takes into account a number of parallel processes, such as the non-stationary influx of latently infected individuals into the region, the passage by individuals of various stages of an infectious disease, the vaccination of the population, and the re-infection of some of the recovered and vaccinated individuals. The duration of stay of individuals in various stages of an infectious disease is described using distributions other than exponential. An algorithm for numerical statistical modeling of the dynamics of the spread of infection among the population of the region based on the Monte Carlo method has been developed. To calibrate the model, we used data describing the level of seroprevalence of the population of the Novosibirsk Region in the first wave of the Covid-19 epidemic in 2020. The results of computational experiments with the model are presented for studying the dynamics of the spread of infection under vaccination of the population of the region.
Key words:
epidemic spread, stage-dependent model, continuous-discrete random process, Monte Carlo method, COVID-19 infection, seroprevalence, computational experiment.
Received 24.05.2022, 10.06.2022, Published 20.06.2022
Bibliographic databases:
Document Type:
Article
Language: Russian
Citation:
N. V. Pertsev, K. K. Loginov, A. N. Lukashev, Yu. A. Vakulenko, “Stochastic modeling of dynamics of the spread of COVID-19 infection taking into account the heterogeneity of population according to immunological, clinical and epidemiological criteria”, Mat. Biolog. Bioinform., 17:1 (2022), 43–81
\Bibitem{PerLogLuk22}
\by N.~V.~Pertsev, K.~K.~Loginov, A.~N.~Lukashev, Yu.~A.~Vakulenko
\paper Stochastic modeling of dynamics of the spread of COVID-19 infection taking into account the heterogeneity of population according to immunological, clinical and epidemiological criteria
\jour Mat. Biolog. Bioinform.
\yr 2022
\vol 17
\issue 1
\pages 43--81
\mathnet{http://mi.mathnet.ru/mbb480}
\crossref{https://doi.org/10.17537/2022.17.43}
\elib{https://elibrary.ru/item.asp?id=49295836}
Linking options:
https://www.mathnet.ru/eng/mbb480
https://www.mathnet.ru/eng/mbb/v17/i1/p43
This publication is cited in the following 5 articles:
I.D. Kolesin, E.M. Zhitkova, “Mathematical Modeling Of the Spread of COVID-19, Taking Into Account the Distribution of Asymptomatic Cases between Actually Asymptomatic and Pre-Symptomatic Cases”, Math.Biol.Bioinf., 19:1 (2024), 52
N. V. Pertsev, V. A. Topchii, K. K. Loginov, “Stochastic modeling of local by time and location contacts of individuals in the epidemic process”, J. Appl. Industr. Math., 17:2 (2023), 355–369
N. V. Pertsev, V. A. Topchii, K. K. Loginov, “Stokhasticheskoe modelirovanie epidemicheskogo protsessa na osnove stadiya-zavisimoi modeli s nemarkovskimi ogranicheniyami dlya individuumov”, Matem. biologiya i bioinform., 18:1 (2023), 145–176
N. V. Pertsev, K. K. Loginov, “Stochastic modeling in immunology based on a stage-dependent framework with non-Markov constraints for individual cell and pathogen dynamics”, Matem. biologiya i bioinform., 18:2 (2023), 543–567
Dmitry Grebennikov, Antonina Karsonova, Marina Loguinova, Valentina Casella, Andreas Meyerhans, Gennady Bocharov, “Predicting the Kinetic Coordination of Immune Response Dynamics in SARS-CoV-2 Infection: Implications for Disease Pathogenesis”, Mathematics, 10:17 (2022), 3154
Citation in format AMSBIB
Contact us: