Analysis of a stage-dependent epidemic model based on a non-Markov random process

Pertsev, N. V.; Loginov, K. K.; Topchii, V. A.

doi:10.33048/sibjim.2020.23.309

Cited by 2 publications

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“…Представленный в работе подход позволяет перейти к марковскому случайному процессу в расширенном пространстве состояний, включающем описание когорт индивидуумов с помощью целочисленных 195 и вещественных переменных. Такой подход к моделированию динамики популяций был предложен в работах [24]- [28] и развит в настоящей статье применительно к моделированию динамики эпидемического процесса.…”

Section: заключениеunclassified

“…По своей формализации, используемому математическому и вычислительному аппарату представленная ниже модель существенно отличается от моделей, перечисленных в обзоре. Для построения модели использован подход, предложенный в работах [24]- [28]. Приведенная ниже модель является развитием и обобщением модели [28] с точки зрения набора переменных, отражающих неоднородность популяции, учета переходов индивидуумов между когортами, вызванных процессами инфицирования и развития заболевания различной тяжести, миграционного притока восприимчивых, латентно-инфицированных и больных индивидуумов.…”

Section: Introductionunclassified

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Direct Statistical Modeling of Spread of Epidemic Based On a Stage-Dependent Stochastic Model

Loginov¹,

Pertsev²

2021

Math.Biol.Bioinf.

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A stochastic stage-dependent model of spread of an epidemic in a certain region is presented. The model is written in the form of a continuous-discrete random process that takes into account the passage of individuals through various stages of an infectious disease. Within the framework of the model, the population of the region is represented in the form of cohorts of individuals, structured according to immunological, clinical, epidemiological and demographic criteria. All cohorts make up two blocks. Individuals belonging to the cohorts of the first block are considered indistinguishable within a fixed cohort and have the same type of parametric description. Individuals belonging to the cohorts of the second block differ from each other by the time of admission to a particular cohort and by the time of stay in this cohort. An algorithm for statistical modeling of the dynamics of cohorts of individuals based on the Monte Carlo method is developed. A numerical study of the dynamics of cohorts of individuals was conducted for sets of parameters reflecting different variants of transmission of infection between individuals.

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Section: заключениеunclassified

Section: Introductionunclassified

Direct Statistical Modeling of Spread of Epidemic Based On a Stage-Dependent Stochastic Model

Loginov¹,

Pertsev²

2021

Math.Biol.Bioinf.

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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

Pertsev¹,

Loginov²,

Lukashev³

et al. 2022

Math.Biol.Bioinf.

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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.

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Analysis of a stage-dependent epidemic model based on a non-Markov random process

Cited by 2 publications

References 0 publications

Direct Statistical Modeling of Spread of Epidemic Based On a Stage-Dependent Stochastic Model

Direct Statistical Modeling of Spread of Epidemic Based On a Stage-Dependent Stochastic Model

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

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