MATHEMATICAL AND COMPUTER MODELS OF THE COVID-19 EPIDEMIC

Authors

DOI:

https://doi.org/10.37943/12QWEH5166

Keywords:

coronavirus, COVID-19, mathematical model, SIR model, computer model, Python, epidemic model

Abstract

The COVID-19 epidemic has gone down in history as an emergency of international importance. Currently, the number of people infected with coronavirus around the world continues to grow, and modeling such a complex system as the spread of infection is one of the most pressing problems. Various models are used to understand the progress of the COVID-19 coronavirus epidemic and to plan effective control strategies. Such models require the use of advanced computing, such as artificial intelligence, machine learning, cloud computing, and edge computing. This article uses the SIR mathematical model, which is often used and simple to model the prevalence of COVID-19 infection. The SIR model can provide a theoretical basis for studying the prevalence of the COVID-19 virus in a specific population and an understanding of the temporal evolution of the virus. One of the main advantages of this model is the ease of adjusting the sampling parameters as the study scale increases and the most appropriate graphs between the data and the resulting assumptions. Computer models based on the mathematical SIR model of the spread of the COVID-19 epidemic make it possible to estimate the number of possible deaths in the future. In addition, on the basis of the proposed models, it will be possible to assess the effectiveness of measures taken to prevent infection by comparing published data with forecasts. Computer models in Python are created on the basis of the proposed mathematical apparatus of SIR. The following libraries were added in the Python high-level programming language for the numerical solution of the system of differential equations for the SIR model: NumPy, Matplotlib PyPlot and the Integrate module from the SciPy library.

Author Biographies

Indira Uvaliуeva, D. Serikbayev East Kazakhstan Technical University

PhD, Associate Professor of the Department of Mathematical and Computer Modeling

Saule Belginova, Turan University

PhD, Associate Professor of the Department of Information Technology

Sanzhar Sovetbekov, D. Serikbayev East Kazakhstan Technical University

Master’s student of the Department of Mathematical and Computer Modeling

References

Nikiforov, V.V., Suranova, T.G., Mironov, A. YU., & Zabozlayev F.G. (2020). Novaya koronavirusnaya infektsiya (COVID-19): etiologiya, epidemiologiya, klinika, diagnostika, lecheniye i profilaktika [New coronavirus infection (COVID-19): etiology, epidemiology, clinic, diagnosis, treatment and prevention]. Internist. Moskva. https://internist.ru/publications/detail/novaya-koronavirusnayainfektsiya-covid-19-etiologiya-epidemiologiya-klinika-lechenie-i-profilaktika/

Starshinova, A.A., Kushnareva, E.A., Malkova, A.M., Dovgalyuk, I.F., & Kudlay D.A. (2020). Novaja koronavirusnaja infekcija:osobennosti klinicheskogo techenija, vozmozhnosti diagnostiki, lechenija i profilaktiki infekcii u vzroslyh i detej. Voprosy sovremennoj pediatrii. [New coronaviral infection: Features of clinical course, capabilities of diagnostics, treatment and prevention in adults and children]. Voprosy sovremennoj pediatrii. 19(2):123-131. https://doi.org/10.15690/vsp.v19i2.2105

Ivanova, A.A., Potashev, A.V., Potasheva, Ye.V. (2016). Matematicheskoye modelirovaniye dinamiki razvitiya epidemiy [Mathematical modeling of epidemic dynamics]. Nauchnoye obozreniye. 24, 269-272.

Matveyev, A.V. (2020). Matematicheskoye modelirovaniye otsenki effektivnosti mer protiv rasprostraneniya epidemii COVID-19 [Mathematical modeling of the effectiveness of measures against the spread of the COVID-19 epidemic]. Natsional’naya bezopasnost’ i strategicheskoye planirovaniye. 1, 23-39.

Navjot, V., & Monica (2022). Coronavirus: A review. Journal of Emerging Technologies and innovative research (JETIR), 9(3), 1-11. https://www.researchgate.net/publication/360312247_CORONAVIRUS_A_REVIEW

Lewis, K. (2022). Understanding youth disconnection in the age of coronavirus. Community Qualityof-Life Indicators. Springer, 105-123. https://doi.org/10.1007/978-3-031-06940-6_7

Mandeep, S., Dhruv, D. (2022). A recent review on: Coronavirus disease 2019. Asian Journal of Pharmaceutical and Clinical Research. Published by Innovare Academic Sciences. https://doi.org/10.22159/ajpcr.2022.v15i7.44547

Chavan, M.B., Tarade, D., Pagare, K.H., & Jain, R.S. (2021). A review on coronavirus. Research Journal of Science and Technology, 13(4), 253-255. https://doi.org/10.52711/2349-2988.2021.00039

Briggs, W.M. (2022). The most infamous coronavirus forecast. In International Conference of the Thailand Econometrics Society (pp. 39-49). Springer, Cham. https://doi.org/10.1007/978-3-030-97273-8_4

Rodkin M.V., Shikhova N.M. (2020). Matematicheskoye modelirovaniye razvitiya epidemii COVID-19, popytka prognoza [Mathematical modeling of the COVID-19 epidemic, an attempt at prediction.]. Ural’skiy geologicheskiy zhurnal, 3, 3-13]

Thomas, D.M., Sturdivant, R., Dhurandhar, N.V., Debroy, S., & Clark, N. (2020). A primer on COVID-19 mathematical models. Obesity, 28(8), 1375-1377. https://doi.org/10.1002/oby.22881

Chen, X., Li, J., Xiao, C., & Yang, P. (2021). Numerical solution and parameter estimation for uncertain SIR model with application to COVID-19. Fuzzy Optimization and Decision Making, 20(2), 189-208. https://doi.org/10.1007/s10700-020-09342-9

Muñoz-Fernández, G.A., Seoane, J.M., & Seoane-Sepúlveda, J.B. (2021). A SIR-type model describing the successive waves of COVID-19. Chaos, Solitons & Fractals, 144, 110682. https://doi.org/10.1016/j.chaos.2021.110682

Comunian, A., Gaburro, R., & Giudici, M. (2020). Inversion of a SIR-based model: a critical analysis about the application to COVID-19 epidemic. Physica D: Nonlinear Phenomena, 413, 132674. https://doi.org/10.1016/j.physd.2020.132674

Ellison, G. (2020). Implications of heterogeneous SIR models for analyses of COVID-19. National Bureau of Economic Research, 27373. http://www.nber.org/papers/w27373.pdf

Townsend, C. (2022). Modeling Coronavirus-19. In A Risky Business (pp. 345-371). Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-11673-5_17

Azam, S., Nauman, A., Raza, A., Muhammad Sajid, I., Rafiq, M., Khan, I., ... & Iqbal, Z. (2021). Numerical analysis of novel coronavirus (2019-nCov) pandemic model with advection. Computers, Materials, & Continua, 2933–2953. https://doi.org/10.32604/cmc.2021.012396

DarAssi, M. H., Shatnawi, T. A., & Safi, M. A. (2022). Mathematical analysis of a MERS-Cov coronavirus model. Demonstratio Mathematica, 55(1), 265-276. https://doi.org/10.1515/dema-2022-0022

Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals. 139, 110057. https://www.sciencegate.app/document/10.1016/j.chaos.2020.110057

Chen, Y.C. et al. (2020). A time-dependent SIR model for COVID-19 with undetectable infected persons. IEEE Transactions on Network Science and Engineering, 7(4), 3279-3294. http://gibbs1.ee.nthu.edu.tw/A_TIME_DEPENDENT_SIR_MODEL_FOR_COVID_19.PDF

Gavrilina A.V., Sokolov S.V. (2018). Analiz SIR-modeli rasprostraneniya zabolevaniy [Analysis of the SIR model of disease transmission]. Protsessy upravleniya i ustoychivost’, 5(1), 229-232.

Moein, S., Nickaeen, N., Roointan, A., Borhani, N., Heidary, Z., Javanmard, S. H., ... & Gheisari, Y. (2021). Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan. Scientific reports, 11(1), 1–9. https://doi.org/10.1038/s41598-021-84055-6

Comunian A., Gaburro R., & Giudici M. (2020). Inversion of a SIR-based model: a critical analysis about the application to COVID-19 epidemic. Physica D: Nonlinear Phenomena, 413, 132674. https://doi.org/10.1016/j.physd.2020.132674

Lee, G. et al. (2019). PyWavelets: A Python package for wavelet analysis. Journal of Open Source Software, 4(36), 1237. https://doi.org/10.21105/joss.01237

Vallat, R. (2018). Pingouin: statistics in Python. Journal of Open Source Software, 3(31), 1026. https://doi.org/10.21105/joss.01026

Downloads

Published

2022-12-30

How to Cite

Uvaliуeva I., Belginova, S., & Sovetbekov, S. (2022). MATHEMATICAL AND COMPUTER MODELS OF THE COVID-19 EPIDEMIC. Scientific Journal of Astana IT University, 12(12), 89–100. https://doi.org/10.37943/12QWEH5166

Issue

Section

Articles
betpas