Dynamics of COVID-19 using inverse problem for coefficient identification in SIR epidemic models | Zendy

Tchavdar Marinov | Zendy; Rossitza S. Marinova | Zendy

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Dynamics of COVID-19 using inverse problem for coefficient identification in SIR epidemic models

Author(s) -

Tchavdar Marinov,

Rossitza S. Marinova

Publication year - 2020

Publication title -

chaos solitons and fractals x

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.457

H-Index - 5

ISSN - 2590-0544

DOI - 10.1016/j.csfx.2020.100041

Subject(s) - covid-19 , pandemic , geography , identification (biology) , epidemic model , infectivity , econometrics , demography , statistics , mathematics , outbreak , population , virology , medicine , biology , infectious disease (medical specialty) , disease , sociology , virus , botany , pathology

This work deals with the inverse problem in epidemiology based on a SIR model with time-dependent infectivity and recovery rates, allowing for a better prediction of the long term evolution of a pandemic. The method is used for investigating the COVID-19 spread by first solving an inverse problem for estimating the infectivity and recovery rates from real data. Then, the estimated rates are used to compute the evolution of the disease. The time-depended parameters are estimated for the World and several countries (The United States of America, Canada, Italy, France, Germany, Sweden, Russia, Brazil, Bulgaria, Japan, South Korea, New Zealand) and used for investigating the COVID-19 spread in these countries.

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