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Ozar P. Mintser
Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine,
Immunology & microbiology Neuroscience

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A simple epidemic model of COVID-19 and its application to Ukrainian, Indonesian, and the global data Serhii O. Soloviov; Mohamad S. Hakim; Iryna V. Dzyublyk; Serhii H. Ubohov; Ozar P. Mintser; Viktor V. Trokhymchuk
Journal of the Medical Sciences (Berkala Ilmu Kedokteran) Vol 52, No 3 (2020): Special Issue: COVID-19
Publisher : Journal of the Medical Sciences (Berkala Ilmu Kedokteran)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.019 KB) | DOI: 10.19106/JMedSciSI005203202001

Abstract

At the beginning of 2020, one of the most significant health problems for humanity is the pandemic of coronavirus disease 2019 (COVID-19). Here, we identify features and develop simple epidemic model of COVID-19 on the basis of available epidemiological data and existing trends worldwide. Modeling of COVID-19 epidemic process was based on a classic model. A key parameter of the model, i.e. transmission parameter of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was determined numerically with the use of available epidemiological daily reports of COVID-19 from 17 April to 23 May 2020. Numerical determination of transmission parameter of SARS-CoV-2 according to the absolute number of COVID-19 cases in Ukraine, Indonesia and worldwide data showed its global tendency to decrease over time. Approximation of the obtained numerical values of the transmission parameter of SARS-CoV-2 was carried out using the exponential function. The results of prognostic modeling showed that by the end of summer 2020, above 30 thousand COVID-19 cases are expected in Ukraine, 100 thousand COVID-19 cases in Indonesia, and 12 million COVID-19 cases worldwide. Thus, predicting the possible consequences of the implementation of various health care control programs COVID-19 involves a comprehensive study of the epidemic process of the disease as a whole and for certain periods of time with the subsequent construction of an adequate prediction model.
Co-Authors Iryna V. Dzyublyk Mohamad S. Hakim Serhii H. Ubohov Serhii O. Soloviov Viktor V. Trokhymchuk