Covid-19 simulation in Malaysia based on susceptible-infected-recovered (SIR) mathematical model / Nurizatul Syarfinas Ahmad Bakhtiar ... [et al.]

• Main Authors: Ahmad Bakhtiar, Nurizatul Syarfinas, Fauzi, Nur Fatihah, Khairudin, Nur Izzati, Ab Halim, Huda Zuhrah
• Format: Article
• Language: English
• Published: Universiti Teknologi MARA, Perak 2023
• Subjects: Electronic Computers. Computer Science; Neural networks (Computer science). Data processing
• Online Access: https://ir.uitm.edu.my/id/eprint/78302/2/78302.pdf; https://ir.uitm.edu.my/id/eprint/78302/; https://mijuitm.com.my/view-articles/
• Institution: Universiti Teknologi Mara

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