We investigate inverse problems of finding unknown parameters ofmathematical models SEIR-HCD and SEIR-D of COVID-19 spread withadditional information about the number of detected cases, mortality,self-isolation coefficient, and tests performed for the city of Moscowand Novosibirsk region since 23.03.2020. In SEIR-HCD the population isdivided into seven groups, and in SEIR-D into five groups with similarcharacteristics and transition probabilities depending on the specificregion of interest. An identifiability analysis of SEIR-HCD is made toreveal the least sensitive unknown parameters as related to theadditional information. The parameters are corrected by minimizing someobjective functionals which is made by stochastic methods (simulatedannealing, differential evolution, and genetic algorithm). Prognosticscenarios for COVID-19 spread in Moscow and in Novosibirsk region aredeveloped, and the applicability of the models is analyzed.

Язык оригиналаанглийский
Страницы (с-по)332-348
Число страниц17
ЖурналNumerical Analysis and Applications
Номер выпуска4
СостояниеОпубликовано - окт 2020


Подробные сведения о темах исследования «Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region». Вместе они формируют уникальный семантический отпечаток (fingerprint).