A Performance of SIR Model in Predicting the Number of COVID-19 Cases in Malaysia based on Different Phase of COVID-19 Outbreak

A Performance of SIR Model in Predicting the Number of COVID-19 Cases in Malaysia based on Different Phase of COVID-19 Outbreak

Authors

  • Nur Fatihah Fauzi Universiti Teknologi MARA, Cawangan Perlis
  • Nurizatul Syarfinas Ahmad Bakhtiar Universiti Teknologi MARA, Cawangan Perlis
  • Nur Izzati Khairudin Universiti Teknologi MARA, Cawangan Perlis
  • Huda Zuhrah Ab. Halim Universiti Teknologi MARA, Cawangan Perlis
  • Nor Hayati Shafii Universiti Teknologi MARA, Cawangan Perlis

DOI:

https://doi.org/10.24191/jcrinn.v8i1.334

Keywords:

SIR model, Covid19, COVID-19, Infection

Abstract

Since December 2019, COVID-19 has quickly taken on a massive global form led to the World Health Organization (WHO) classified COVID-19 as a pandemic outbreak as a result. Due to a lack of information about the virus and the absence of medical services in the community during the early stages of this outbreak, the coronavirus spread quickly. Consequently, it becomes extremely difficult to control the influence of the disease outbreak. Thus, this study was aim to predict the peak numbers of the infected population on the first, the second, the third waves and endemic phase by utilizing a Susceptible-Infected-Recovered (SIR) predictive model between 25th January and 16th February 2020, corresponding to the entire first wave, the second, between 27th February 2020 and 30th June 2020, corresponding to part of the second wave, the current third wave began on 7th September 2020 and 1st April 2022, corresponding to the endemic phase, still present at the time of writing this article. The model retrieved the data from a reliable source on the Internet and its design is based on certain assumptions. The estimated reproductive value  in this model for all simulations is 5.1, which are interconnected factors that have contributed to the rapid increase in the number of COVID-19 cases. This led to the outbreak of a highly contagious disease. The number of infected populations increase with the rate of disease transmission rate of infected  increases and vice versa and the spread of COVID-19 from first, second, third waves and endemic reached maximum level in a very short time. The COVID-19 endemic in Malaysia is predicted to peak by the early of March 2020 for the first wave, mid of April 2020 for the second wave, early of October 2020 for the third wave and around April 22, 2022, for the endemic phase. The peak infected was predicted at 16,280,000 persons out of a total of susceptible individuals 33,573,874. Therefore, in addition to maintaining control measures at least until the anticipated peak time has passed, proper crisis management and efficient resource use are essential for successfully combating the endemic.

Downloads

Download data is not yet available.

Author Biographies

Nur Izzati Khairudin, Universiti Teknologi MARA, Cawangan Perlis

 

 

 

Huda Zuhrah Ab. Halim, Universiti Teknologi MARA, Cawangan Perlis

 

 

Nor Hayati Shafii, Universiti Teknologi MARA, Cawangan Perlis

 

 

 

References

Ahmad, R., & Budin, H. (2012). Stability analysis of mutualism population model with time delay. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 6(2), 151-155.

Clark, A. (2005). S-I-R model of epidemics part 1: Basic model and examples. Retrieved August, 31, 2022, from https://kipdf.com/s-i-r-model-of-epidemics-part-1-basic-model-and-examples-revised-september-22-20_5adb8af17f8b9aaa418b45f4.html

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.

Diekmann, O., Heesterbeek, J. A. P., & Roberts, M. G. (2010). The construction of next-generation matrices for compartmental epidemic models. Journal of The Royal Society Interface, 7, 873-885.

Egonmwan, A. O., & Okuonghae, D. (2019). Analysis of a mathematical model for tuberculosis with diagnosis. Journal of Applied Mathematics and Computing, 59(1), 129-162.

Github Website. (2022). owid/covid-19-data. Retrieved August 30, 2022, from https://github.com/owid/covid-19-data/tree/master/public/data/

Goel, N. S., Maitra, S. C., & Montroll, E. W. (1971). On the Volterra and other nonlinear models of interacting populations. Reviews of Modern Physics, 43(2), 231.

Hashim, J. H., Adman, M. A., Hashim, Z., Mohd Radi, M. F., & Kwan, S. C. (2021). COVID-19 epidemic in Malaysia: Epidemic progression, challenges, and response. Frontiers in Public Health, 9, 560592.

Hui, D. S., Azhar, E. I., Madani, T. A., Ntoumi, F., Kock, R., Dar, O., & Petersen, E. (2020). 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, 91, 264-266.

Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., & Eggo, R. M. (2020). Early dynamics of transmission and control of COVID-19: A mathematical modelling study. The Lancet Infectious Diseases, 20(5), 553-558.

Levin, S. A., Hallam, T. G., & Gross, L. J. (1989). Applied mathematical ecology. Berlin; New York: Springer-Verlag

Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., ... & Feng, Z. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus - Infected pneumonia. New England Journal of Medicine, 382, 1199-1207

Panovska-Griffiths, J. (2020). Can mathematical modelling solve the current Covid-19 crisis? BMC Public Health, 20(1), 1-3.

Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., & Davies, N. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. The Lancet Public Health, 5(5), 261-270.

Salman, A. M., Ahmed, I., Mohd, M. H., Jamiluddin, M. S., & Dheyab, M. A. (2021). Scenario analysis of COVID-19 transmission dynamics in Malaysia with the possibility of reinfection and limited medical resources scenarios. Computers in Biology and Medicine, 133, 104372.

Side, S., Mulbar, U., Sidjara, S., & Sanusi, W. (2017, April). A SEIR model for transmission of tuberculosis. AIP Conference Proceedings, 1830(1), 020004.

Supramanian, R. K., Sivaratnam, L., Rahim, A. A., Abidin, N. D. I. Z., Richai, O., Zakiman, Z., Taib, S. M., Soo, L., Jamalullai, S. H. S. I., Khirusalleh, M. N. A., & Yusof, M. P. (2021). Descriptive epidemiology of the first wave of COVID-19 in Petaling District, Malaysia: Focus on asymptomatic transmission. Western Pacific Surveillance and Response Journal: WPSAR, 12(2), 82-88.

Takahashi, A., Spreadbury, J., & Scotti, J. (2010). Modeling the spread of tuberculosis in a closed population.

Tang, B., Wang, X., Li, Q., Bragazzi, N. L., Tang, S., Xiao, Y., & Wu, J. (2020). Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9(2), 462.

Tuite, A. R., Fisman, D. N., & Greer, A. L. (2020). Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. Canadian Medical Association Journal, 192(19), 497-505.

Waziri, A. S., Massawe, E. S., & Makinde, O. D. (2012). Mathematical modelling of HIV/AIDS dynamics with treatment and vertical transmission. Applied Mathematics, 2(3), 77-89.

World Health Organization. (2021). Coronavirus disease (COVID-19). WHO | World Health Organization. Retrieved July 15, 2022, from https://www.who.int/healthtopics/coronavirus#tab=tab_1

World Health Organization (2020). Naming the coronavirus disease (COVID-19)) and the virus that causes it. Retrieved 28 February, 2020, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it

Downloads

Published

2023-02-28

How to Cite

Fauzi, N. F., Ahmad Bakhtiar, N. S., Khairudin, N. I., Ab. Halim, H. Z., & Shafii, N. H. (2023). A Performance of SIR Model in Predicting the Number of COVID-19 Cases in Malaysia based on Different Phase of COVID-19 Outbreak. Journal of Computing Research and Innovation, 8(1), 75–83. https://doi.org/10.24191/jcrinn.v8i1.334

Issue

Section

General Computing

Most read articles by the same author(s)

1 2 > >> 

Similar Articles

You may also start an advanced similarity search for this article.

Loading...