This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios epidemic spread in New York State (USA), the UK, Novosibirsk region (Russia). Epidemiological parameters such as contagiousness (virus transmission rate), initial number infected people, probability being tested depend on region's demographic geographical features, containment measures introduced; they are calibrated data about COVID-19 interest. At first stage our study, epidemiological (numbers...

We investigate inverse problems of finding unknown parameters mathematical models SEIR-HCD and SEIR-D COVID-19 spread with additional information about the number detected cases, mortality, self-isolation coefficient, tests performed for city Moscow Novosibirsk region since 23.03.2020. In population is divided into seven groups, in five groups similar characteristics transition probabilities depending on specific interest. An identifiability analysis made to reveal least sensitive as related...

Abstract Four simple mathematical models of pharmacokinetic, competition between immune and tumor cells, infectious disease tuberculosis epidemic are considered. An optimization approach for identification those based on gradient type methods is introduced. Inverse problems formulated in the form an operator equation then reduced to minimization corresponding misfit functionals. The adjoint used calculation gradients. A model cells considered numerically. results a numerical experiment demonstrated.

Abstract The problem of identification unknown epidemiological parameters (contagiosity, the initial number infected individuals, probability being tested) an agent-based model COVID-19 spread in Novosibirsk region is solved and analyzed. first stage modeling involves data analysis based on machine learning approach that allows one to determine correlated datasets performed PCR tests daily diagnoses detect some features (seasonality, stationarity, correlation) be used for modeling. At second...

Abstract This paper presents classification and analysis of the mathematical models spread COVID-19 in different groups population such as family, school, office (3–100 people), town (100–5000 city, region (0.5–15 million country, continent, world. The covers major types (time-series, differential, imitation ones, neural networks their combinations). time-series are based on time series using filtration, regression network methods. differential those derived from systems ordinary stochastic...

The paper presents the results of sensitivity-based identifiability analysis COVID-19 pandemic spread models in Novosibirsk region using systems differential equations and mass balance law. algorithm is built on sensitivity matrix methods linear algebra. It allows one to determine parameters that are least most sensitive data changes build a regularization for solving an identification problem accurate scenarios region. performed has demonstrated virus contagiousness identifiable from number...

Abstract The inverse problem for SEIR-HCD model of COVID-19 propagation in Novosi- birsk region described by system seven nonlinear ordinary differential equations (ODE) is numerical investigated. consists identification coefficients ODE (infection rate, portions infected, hospitalized, mortality cases) and some ini- tial conditions (initial number asymptomatic symptomatic infectious) additional measurements about daily diagnosed, critical cases COVID-19. Due to ill-posedness the...

Abstract. A numerical method for solving the Dirichlet problem wave equation in two-dimensional space is proposed. The analyzed ill-posedness and a regularization algorithm constructed. first stage process consists Fourier series expansion with respect to one of variables passing finite sequence problems one-dimensional space. Each obtained reduced inverse certain direct (well-posed) problem. degree based on character decreasing singular values operator . solution minimizing objective...

Abstract The monitoring, analysis and prediction of epidemic spread in the region require construction mathematical model, big data processing visualization because amount population size could be huge. One important steps is refinement i.e. determination initial coefficients system differential equations epidemiologic processes using additional information. We analyze numerical method for solving inverse problem epidemiology based on genetic algorithm traditional optimization approach. Our...

Abstract A new combined numerical algorithm for solving inverse problems of epidemiology is described in this paper. The consists optimization and iterative methods, determines the parameters specific to a particular population by using statistical information few previous years. coefficients model describe qualities development disease. problem parameter identification mathematical reduced minimizing an objective function characterizing square deviation data from experimental data....

Analysis of biological data is a key topic in bioinformatics, computational genomics, molecular modeling and systems biology. The methods covered this article could reduce the cost experiments for data. problem identifiability mathematical models physiology, pharmacokinetics epidemiology considered. processes considered are modeled using nonlinear ordinary differential equations. Math dynamic based on use mass conservation law. While addressing estimation parameters characterizing process...

Abstract In this paper a problem of specifying HIV-infection parameters and immune response using additional measurements the concentrations T-lymphocytes, free virus effectors at fixed times for mathematical model HIV dynamics is investigated numerically. The parameter (an inverse problem) reduced to minimizing an objective function describing deviation simulation results from experimental data. A genetic algorithm solving least squares minimization implemented investigated. numerical...