Evaluation of the main parameters of the compartmental model of the epidemic development based on the example of the spread of the COVID-19 pandemic in Chernivtsi region
DOI:
https://doi.org/10.26641/2307-0404.2023.1.276215Ключевые слова:
compartmental model (SIR), COVID-19, computer simulation, Chernivtsi regionАннотация
The paper considers the application of the theoretical model of epidemiological development of COVID-19 disease among the regional population based on the statistical data in Chernivtsi region of Ukraine for the period from March 2020 to June 2021. Using these data, a mathematical assessment of the values of the main parameters of the compartmental model (SIR) β and γ was performed and the analysis of the relationship between the values of β and γ and antiepidemiological measures was carried out for the region. Determining the parameters β and γ based on available statistics allows us to predict the duration of precautionary measures such as quarantine, lockdown, border closure and predict the effectiveness of their implementation as well. The analysis of statistical data showed the moderate effectiveness of quarantine and lockdown in changing the daily rates of infected and recovered people, while the dynamics of the epidemic development during these periods changed from negative to positive. The introduction of vaccination has shown the significant reduction in the daily rate of infected people and the substantial increase in the daily rate of the recovered people.
Библиографические ссылки
Cucinotta D, Vanelli M. WHO declares COVID-19 a pandemic. Acta Biomed. 2020;91(1):157-60. doi: https://doi.org/10.23750/abm.v91i1.9397
Shrock E, Fujimura E, Kula T, et al. Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity. Science. 2020;370(6520):1-15. doi: https://doi:10.1126/science.abd4250
Ge Y, Martinez L, Sun S, et al. COVID-19 Transmission Dynamics Among Close Contacts of Index Patients With COVID-19: A Population-Based Cohort Study in Zhejiang Province, China. JAMA Intern Med. 2021;181(10)1343-50. doi: https://doi:10.1001/jamainternmed.2021.4686
Ng OT, Marimuthu K, Koh V, et al. SARS-CoV-2 seroprevalence and transmission risk factors among high-risk close contacts: a retrospective cohort study. The Lancet Infectious Diseases. 2021;21(3):333-43. doi: https://doi.org/10.1016/S1473-3099(20)30833-1
Rothe C, Schunk M, Sothmann P, Bretzel G, et al. Transmission of 2019-nCoV Infection from an asymptomatic contact in Germany. N Engl J Med. 2020 Mar 5;382(10):970-1. doi: https://doi.org/10.1056/NEJMc2001468
Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc Lond. Ser. A. 1927;115:700-21. doi: https://doi.org/10.1098/rspa.1927.0118
Smith D, Moore L. The SIR Model for Spread of Disease – The Differential Equation Model. Convergence. 2004 Dec.
covid19.rnbo.gov.ua [Internet]. [The website of the National Security and Defense Council of Ukraine (NSDO).] [cited 15 Jun 2021]. Ukrainian. Available from: https://covid19.rnbo.gov.ua
Devore JL. Probability and Statistics for Engineering and the Sciences. 8th ed. Boston, MA: Cengage Learning; 2011. р. 508-10.
Turkyilmazoglu M. Explicit formulae for the peak time of an epidemic from the SIR model. Physica D. 2021 Mar 26;422:132902. doi: https://doi.org/10.1016/j.physd.2021.132902
Kyrychko YN, Blyuss KB, Brovchenko I. Mathematical modelling of the dynamics and containment of COVID-19 in Ukraine. Sci Rep. 2020;10:19662. doi: https://doi.org/10.1038/s41598-020-76710-1
Cano C. Louisiana Tech University: Louisiana Tech Digital Commons Repository [Internet]. 2020 [cited 15 Jun 2021]. Available from: http://network.bepress.com/physical-sciences-and-mathematics/mathematics/dynamical-systems
Desouky ED. Prediction of the epidemic peak of Covid19 in Egypt, 2020. MedRxiv. 2020;04.30;20086751. doi: https://doi.org/10.1101/2020.04.30.20086751
Sharov KS. Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency. Chaos Solitons Fractals. 2020 Sep 24;141:110295. doi: https://doi.org/10.1016/j.chaos.2020.110295
Singh R, Lal R, Kotti R. Timediscrete SIR model for COVID-19 in Fiji. Epidemiology and Infection. 2022;150:E75. doi: https://doi.org/10.1017/S0950268822000590
Загрузки
Опубликован
Как цитировать
Выпуск
Раздел
Лицензия
Copyright (c) 2023 Medicni perspektivi
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Submitting manuscript to the journal "Medicni perspektivi" the author(s) agree with transferring copyright from the author(s) to publisher (including photos, figures, tables, etc.) editor, reproducing materials of the manuscript in the journal, Internet, translation into other languages, export and import of the issue with the author’s article, spreading without limitation of their period of validity both on the territory of Ukraine and other countries. This and other mutual duties of the author and all co-authors separately and editorial board are secured by written agreement by special form to use the article, the sample of which is presented on the site.
Author signs a written agreement and sends it to Editorial Board simultaneously with submission of the manuscript.
