A simple epidemic model of COVID-19 and its application to Ukrainian, Indonesian, and the global data

https://doi.org/10.19106/JMedSciSI005203202001

Serhii O. Soloviov(1), Mohamad S. Hakim(2*), Iryna V. Dzyublyk(3), Serhii H. Ubohov(4), Ozar P. Mintser(5), Viktor V. Trokhymchuk(6)

(1) Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine,
(2) Department of Microbiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada
(3) Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine,
(4) Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine,
(5) Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine,
(6) Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine,
(*) Corresponding Author

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.


Keywords


COVID-19; epidemiology; mathematical modeling; prediction;

Full Text:

PDF


References

  1. Peiris JSM, Lai ST, Poon LL, Guan Y, Yam LYC, Lim W, et al. Coronavirus as a possible cause of severe acute respiratory syndrome. Lancet 2003; 361(9366):1319-25. https://doi.org/10.1016/s0140-6736(03)13077-2
  2. Drosten C, Gunther S, Preiser W, Werf SRV, Brodt HN, Becker S, et al. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N Engl J Med 2003; 348(20):1967-76. https://doi.org/10.1056/NEJMoa030747
  3. Zhong N, Zeng G. What we have learnt from SARS epidemics in China. BMJ 2006; 333(7564):389-391. https://doi.org/10.1136/bmj.333.7564.389
  4. Widagdo W, Okba NMA, Stalin Raj V, Haagmans BL. MERS-coronavirus: from discovery to intervention. One Health 2017; 3:11-6. https://doi.org/10.1016/j.onehlt.2016.12.001
  5. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020; 382(8):727-33. https://doi.org/10.1056/NEJMoa2001017
  6. Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020; 579(7798):270-73. https://doi.org/10.1038/s41586-020-2012-7
  7. Coronaviridae Study Group of the International Committee on Taxonomy of V. The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020; 5(4):536-44. https://doi.org/10.1038/s41564-020-0695-z
  8. Jin Y, Yang H, Ji W, Wu W, Chen S, Zhang W, et al. Virology, epidemiology, pathogenesis, and control of COVID-19. Viruses 2020; 12(4):372. https://doi.org/10.3390/v12040372
  9. Zheng J. SARS-CoV-2: an emerging coronavirus that causes a global threat. Int J Biol Sci 2020; 16(10):1678-85. https://doi.org/10.7150/ijbs.45053
  10. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
  11. https://www.worldometers.info/coronavirus/?utm_campaign=homeAdvegas1?
  12. Yang Y, Peng F, Wang R, Yange M, Guan K, Jiang T, et al. The deadly coronaviruses: the 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. J Autoimmun 2020; 109:102434. https://doi.org/10.1016/j.jaut.2020.102434
  13. Wangping J, Ke H, Yang S, Wenzhe C, Shengshu W, Shanshan Y, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Front Med 2020; 7:169. https://doi.org/ 10.3389/fmed.2020.00169
  14. Kumar K, Meitei WB, Singh A. Projecting the future trajectory of COVID-19 infections in India using the susceptible-infected-recovered (SIR) model. 2020. 1-21. https://iipsindia.ac.in/sites/default/files/iips_covid19_pfti.pdf
  15. Dandekar R, Barbastathis G. Neural network aided quarantine control model estimation of global Covid-19 spread. arXiv preprint, arXiv:2004.02752 (2020).
  16. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020; 20(5):533-4. https://doi.org/10.1016/S1473-3099(20)30120-1
  17. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London 1927; 115(772):700-21. https://doi.org/10.1098/rspa.1927.0118
  18. Coronavirus (COVID-19): https://covid.ourworldindata.org/data/owid-covid-data.xlsx
  19. Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 2020; 20(5):553-8. https://doi.org/10.1016/S1473-3099(20)30144-4
  20. Liu Y, Gayle AA, Smith AW, Rocklov J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med 2020; 27(2):taaa021. https://doi.org/10.1093/jtm/taaa021
  21. Bai Y, Yao L, Wei T, Tian F, Jin DY, Chen L, et al. Presumed asymptomatic carrier transmission of COVID-19. JAMA 2020; 323(14):1406-7. https://doi.org/10.1001/jama.2020.2565



DOI: https://doi.org/10.19106/JMedSciSI005203202001

Article Metrics

Abstract views : 3829 | views : 2464


Bookmark and Share


Copyright (c) 2020 Serhii O. Soloviov, Mohamad S. Hakim, Iryna V. Dzyublyk, Serhii H. Ubohov, Ozar P. Mintser, Viktor V. Trokhymchuk

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.