A data-driven SIR model analysis of COVID-19 interventions in Malaysia / Abdul Basit ... [et al.]
COVID-19 emerged in late 2019 and rapidly spread worldwide, posing a significant challenge to effective outbreak management. This study employs the Susceptible-Infected-Recovered (SIR) model to understand the transmission dynamics of COVID-19 in Malaysia, with a population of 34.3 million. Focusing...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Universiti Teknologi MARA, Perak
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/114736/1/114736.pdf https://ir.uitm.edu.my/id/eprint/114736/ https://mijuitm.com.my/wp-content/uploads/2025/04/4.-A-Data-Driven-SIR-Model-Analysis-of-COVID-19-Interventions-in-Malaysia.pdf |
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| Summary: | COVID-19 emerged in late 2019 and rapidly spread worldwide, posing a significant challenge to effective outbreak management. This study employs the Susceptible-Infected-Recovered (SIR) model to understand the transmission dynamics of COVID-19 in Malaysia, with a population of 34.3 million. Focusing on key phases implemented like the Movement Control Order (MCO) and Recovery Movement Control Order (RMCO), the research evaluates infection rates, recovery dynamics, and reproduction numbers using real-world data from the Johns Hopkins University COVID-19 Dashboard. During the MCO phase (18 March 2020 to 3 May 2020), the transmission rate was 0.0806, the recovery rate was 0.0309, and the basic reproduction number (R0) was 2.607, with 90.52% of the population remaining susceptible post-phase. The RMCO phase (10 June 2020 to 31 March 2021) saw reduced transmission and recovery rates of 0.0880 and 0.0518, respectively, resulting in an R0 of 1.698 and 68.97% of the population remaining susceptible. The peak infection rate during RMCO was significantly lower (1.698%), with the infection peak forecasted for 11 December 2020. The findings offer actionable insights for policymakers, demonstrating how targeted lockdown measures can significantly reduce transmission rates and delay infection peaks while emphasizing the SIR model's utility in providing timely insights during evolving public health crises. |
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