Mathematical models for transmission dynamics of the novel COVID-2019 coronavirus, an outbreak of which began in December, 2019, in Wuhan are considered. To control the epidemiological situation, it is necessary to develop corresponding mathematical models. Mathematical models of COVID-2019 spread described by systems of nonlinear ordinary differential equations (ODEs) are overviewed. Some of the coefficients and initial data for the ODE systems are unknown or their averaged values are specified. The problem of identifying model parameters is reduced to the minimization of a quadratic objective functional. Since the ODEs are nonlinear, the solution of the inverse epidemiology problems can be nonunique, so approaches for analyzing the identifiability of inverse problems are described. These approaches make it possible to establish which of the unknown parameters (or their combinations) can be uniquely and stably recovered from available additional information. For the minimization problem, methods are presented based on a combination of global techniques (covering methods, nature-like algorithms, multilevel gradient methods) and local techniques (gradient methods and the Nelder–Mead method).

Original languageEnglish
Pages (from-to)1889-1899
Number of pages11
JournalComputational Mathematics and Mathematical Physics
Issue number11
Publication statusPublished - Nov 2020


  • coronavirus
  • COVID-2019
  • epidemiology
  • gradient methods
  • identifiability
  • inverse problems
  • mathematical models
  • nature-like algorithms
  • ODE
  • optimization
  • regularization
  • tensor decomposition




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