PREDICTION MODEL OF CORONAVIRUS INFECTION COVID-19 EPIDEMIC PROCESS IN UKRAINE

Authors

  • S. O. Soloviov Shupyk National Medical Academy of Postgraduate Education, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»
  • I. V. Dziublyk Shupyk National Medical Academy of Postgraduate Education
  • O. P. Mintser Shupyk National Medical Academy of Postgraduate Education https://orcid.org/0000-0002-7224-4886

DOI:

https://doi.org/10.11603/mie.1996-1960.2020.2.11176

Keywords:

coranavirus infection, COVID-19, epidemiology, mathematical modeling, prediction

Abstract

Background. Identification of features and development of prediction model of COVID-19 epidemic process in Ukraine on the basis of available epidemiological data and existing trends. Modeling of COVID-19 epidemic process was based on classic epidemiological model.

Materials and methods. Key parameter of the model — transmission parameter of SARS-COV2 was determined numericaly with the use of avalable epidemiological data: daily reports of the Ministry of Health of Ukraine of the absolute number of patients with COVID-19. Numerical determination of transmission parameter of SARS-COV2 according to the absolute number of patients with COVID-19 in each region and in Ukraine shOwed its tendency to decrease over time. Approximation of the obtained numerical values of the transmission parameter o SARS-COV2 was carried oOut between April 7 and May 2, 2020 using the exponential function.

Results. The results of pragnostic modeling showed that by the end of summer 2020 about 25 thousand people with COVID-19 are expected, and the peak incidence occurs at the time of the study (April 28 — May 5, 2020). In addition, research allowed us to analyze the intensity of the epidemic process in different regions of Ukraine on the basis of  the calculated average values of SARS-COV2 transmission in the period from April 7 to May 2, 2020. It was determined that the most intensive epidemic process is in Kharkiv, Luhansk and Mykolayiv regions, which can be useful information for making appropriate management decisions to deepen quarantine measures in these regions.

Conclusions. 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. We praposed a simple prediction model, but effective tool for predicting the epidemic process COVID-19 that can be useful in the practical work of health professionals.

References

Peiris, J. S. M, Lai, S. T., Poon, L. L. M. et al. (2003). Coronavirus as a possible cause of severe acute respiratory syndrome. Lancet, 361, 1319-25.

Drosten, C., Ginther, S., Preiser, W. et al. (2003). Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N. Engl. J. Med., 348, 967-76.

Dzyublyk, I. V., Kukalo, O. V. (2015). New human coronaviruses and respiratory diseases. Ukr. J. Pulmon., 4, 53-9.

Novel Coronavirus 2019-nCoV. URL: https//www.who. int/emergencies/diseases/novel-coronavirus-2019 (last accessed 29.01.2020).

Coronavirus disease (COVID-19) Pandemic. URL: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (date of appeal: 20.03.2020).

Coronavirus Update (Live) URL: https:// www.worldometers.info/coronavirus/?utm_ campaign=homeAdvegas1? (date of appeal: 05.05.2020).

Coronavirus infection COVID-19. Public Health Center URL: https://www.phc.org.ua/kontrol-zakhvoryuvan/ inshi-infekciyni-zakhvoryuvannya/koronavirusna-infekciya-covid-19 (date of appeal: 05.05.2020).

Dietz, K. (1967). Epidemics and Rumours: A Survey. J. Royal Stat. Soc, 130, 505-28.

Kermack, W. O., McKendrick, A. G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proc. Royal Soc. London, 115, 700-21.

Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y. et al. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis. 20, 553-8. doi: 10.1016/S1473-3099(20)30144-4.

Endo, A., Abbott, S. et al. (2020). Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China [version 1; peer review: 1 approved]. Wellcome Open Res., 5, 67.

Liu, Y., Gayle, A. A., Wilder-Smith, A., Rocklov, J. (2020). The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med., 27 (2), taaa021. doi: 10.1093/jtm/taaa021.

Published

2020-07-13

How to Cite

Soloviov, S. O. ., Dziublyk, I. V. ., & Mintser, . O. P. (2020). PREDICTION MODEL OF CORONAVIRUS INFECTION COVID-19 EPIDEMIC PROCESS IN UKRAINE. Medical Informatics and Engineering, (2), 70–78. https://doi.org/10.11603/mie.1996-1960.2020.2.11176

Issue

Section

Articles