Covid-19 Simulation in Malaysia Based on Susceptible-Infected-Recovered (SIR) Mathematical Model

  • Nurizatul Syarfinas Ahmad Bakhtiar UiTM CAWANGAN PERLIS
  • Nur Fatihah Fauzi UiTM CAWANGAN PERLIS
  • Nur Izzati Khairudin UiTM CAWANGAN PERLIS
  • Huda Zuhrah Ab Halim

Abstract

The outbreak of Covid-19 in Wuhan in December 2019 shocked the world. After almost two years, Malaysia's Prime Minister announced in March 2022 that Malaysia was transitioning into the endemic phase of the disease on 1 April 2022. This study aims to investigate the spread of Covid-19 in Malaysia during the endemic phase by developing a susceptible-infected-recovered (SIR) model. The model was used to analyze the reproductive number based on the current number of infected individuals in Malaysia. The results indicate that the maximum number of infected individuals was reached within 50 days after the announcement of the endemic phase. The SIR model confirmed the endemic phase with a reproductive number of 5.1, which is greater than 0. The study also explored the impact of an increase or decrease in the transmission rate on the number of infected individuals during the endemic phase. The simulation results showed that the peak number of infected individuals was initially projected to be 16,540,000 persons on day-21, and this number was directly proportional to changes in the infected population. By formulating a mathematical model and analyzing the stability of its equilibria, the study provides a framework for understanding the dynamics of Covid-19 transmission in Malaysia. This can help policymakers and healthcare professionals to develop more effective interventions and strategies to manage the disease

References

[1] S. Kannan, P. S. S. Ali, A. Sheeza, and K. Hemalatha, “COVID-19 (Novel Coronavirus 2019)-recent trends,” Eur Rev Med Pharmacol Sci, vol. 24, no. 4, pp. 2006–2011, 2020.
[2] W. H. Organization and others, “Coronavirus disease 2019 (COVID-19): situation report, 73,” 2020.
[3] A. Di Castelnuovo et al., “Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study,” Nutr. Metab. Cardiovasc. Dis., vol. 30, no. 11, pp. 1899–1913, 2020.
[4] A. J. Kucharski et al., ‘Early dynamics of transmission and control of COVID-19: a mathematical modelling study’, Lancet Infect Dis, vol. 20, no. 5, pp. 553–558, May 2020, doi: 10.1016/S1473-3099(20)30144-4.
[5] A. R. Tuite, D. N. Fisman, and A. L. Greer, ‘Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada’, CMAJ, vol. 192, no. 19, pp. E497–E505, May 2020, doi: 10.1503/cmaj.200476.
[6] J. Panovska-Griffiths, ‘Can mathematical modelling solve the current Covid-19 crisis?’, BMC Public Health, vol. 20, no. 1, Apr. 2020, doi: 10.1186/s12889-020-08671-z.
[7] K. Prem et al., ‘The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study’, Lancet Public Health, vol. 5, no. 5, pp. e261–e270, May 2020, doi: 10.1016/S2468-2667(20)30073-6.
[8] D. S. Hui et al., ‘The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health — The latest 2019 novel coronavirus outbreak in Wuhan, China’, International Journal of Infectious Diseases, vol. 91. Elsevier B.V., pp. 264–266, Feb. 01, 2020. doi: 10.1016/j.ijid.2020.01.009.
[9] N. S. Goel, S. C. Maitra, E. W. Montroll, V. Model, E. Theory, and T. Time Derivatives, ‘On the Volterra and other nonlinear models of interacting populations. Reviews of modern physics’, 1971.
[10] Q. Li et al., ‘Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia’, New England Journal of Medicine, vol. 382, no. 13, pp. 1199–1207, Mar. 2020, doi: 10.1056/nejmoa2001316.
[11] ‘Naming the coronavirus disease (COVID-19) and the virus that causes it’, World Health Organization, 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-
[12] A. O. Egonmwan and D. Okuonghae, ‘Mathematical analysis of a tuberculosis model with imperfect vaccine’, International Journal of Biomathematics, vol. 12, no. 7, Oct. 2019, doi: 10.1142/S1793524519500736.
[13] A. S. Waziri, E. S. Massawe, and O. Daniel Makinde, ‘Mathematical Modelling of HIV/AIDS Dynamics with Treatment and Vertical Transmission’, Journal Applied Mathematics, vol. 2, no. 3, pp. 77–89, Aug. 2012, doi: 10.5923/j.am.20120203.06.
[14] A. Takahashi, J. Spreadbury, and J. Scotti, ‘Modeling the Spread of Tuberculosis in a Closed Population’.
[15] B. Tang et al., ‘Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions’, J Clin Med, vol. 9, no. 2, Feb. 2020, doi: 10.3390/jcm9020462.
[16] S. Side, U. Mulbar, S. Sidjara, and W. Sanusi, ‘A SEIR model for transmission of tuberculosis’, in AIP Conference Proceedings, Apr. 2017, vol. 1830. doi: 10.1063/1.4980867.
[17] R. Ahmad, H. Budin, and S. Ismail, ‘Stability analysis of mutualism model with time delay and proportional harvesting’, in 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 - Proceedings, Oct. 2016, pp. 464–469. doi: 10.1109/ISMSC.2015.7594099.
[18] I. Cooper, A. Mondal, and C. G. Antonopoulos, ‘A SIR model assumption for the spread of COVID-19 in different communities’, Chaos Solitons Fractals, vol. 139, Oct. 2020, doi: 10.1016/j.chaos.2020.110057.
[19] A. M. Salman, I. Ahmed, M. H. Mohd, M. S. Jamiluddin, and M. A. Dheyab, ‘Scenario analysis of COVID-19 transmission dynamics in Malaysia with the possibility of reinfection and limited medical resources scenarios’, Comput Biol Med, vol. 133, Jun. 2021, doi: 10.1016/j.compbiomed.2021.104372.
[20] S. Zenian, ‘The SIR Model for COVID-19 in Malaysia’, in Journal of Physics: Conference Series, 2022, vol. 2314, no. 1. doi: 10.1088/1742-6596/2314/1/012007.
[21] Simon. A. Levin, Thomas. G. Hallam, and Louis. J. Gross, Applied Mathematical Ecology, vol. 18. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. doi: 10.1007/978-3-642-61317-3.
[22] Github Malaysia, ‘Open data on COVID-19 in Malaysia’, 2022. https://github.com/MoH-Malaysia/covid19-public
[23] A. Clark, ‘S-I-R Model Of Epidemics Part 1 Basic Model And Examples’, 2002. https://zbook.org/read/bdf1f_s-i-r-model-of-epidemics-part-1-basic-model-and-examples.html
[24] O. , Diekmann and J. A. P. Heesterbeek, ‘Mathematical Epidemiology of Infectious Diseases: ModelBuilding, Analysis and Interpretation’, 2000.
Published
2023-05-05
How to Cite
AHMAD BAKHTIAR, Nurizatul Syarfinas et al. Covid-19 Simulation in Malaysia Based on Susceptible-Infected-Recovered (SIR) Mathematical Model. Mathematical Sciences and Informatics Journal, [S.l.], v. 4, n. 1, p. 23-32, may 2023. ISSN 2735-0703. Available at: <https://myjms.mohe.gov.my/index.php/mij/article/view/19380>. Date accessed: 04 oct. 2024. doi: https://doi.org/10.24191/mij.v4i1.19380.
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