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

• Published in: Chaos, Solitons & Fractals: X Solitons & Fractals: X, 2020-03, Vol.5, p.100041-100041, Article 100041
• Main Authors: Marinov, Tchavdar T., Marinova, Rossitza S.
• Format: Article
• Language: English
• ISSN: 2590-0544; 2590-0544
• DOI: 10.1016/j.csfx.2020.100041

•The inverse problem for estimating the time-dependent transmission and removal rates in the SIR epidemic model is derived and solved. The minimization problem uses the entire dataset with data available on June 21, 2020 for estimating the non-constant rates. The obtained numerical results demonstrate that the transmission and removal rates and the unknown functions are accurately estimated.•The numerically computed rates are used for forecasting the COVID-19 pandemic for the world and a number of countries. The results of this research give insight of the pandemic in parts of the world and could help in determining policy. The SIR model is a good choice for the short period of time of this epidemic; however, it possesses known limitations in case of a long term infectious disease. In future, we plan to use other models. Depending on future developments of the disease, we may consider models addressing non-constant population, latency, reinfection, and vaccine. 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.