SEIR-Based Modelling of COVID-19 Spread in Malaysia: A Mathematical Approach to Public Health Interventions and Optimal Control Solutions

The COVID-19 pandemic has posed unprecedented challenges to public health systems globally, requiring the adoption of various control measures to mitigate viral spread. This study aims to model the transmission dynamics of COVID-19 in Malaysia and evaluate the effectiveness of key intervention strategies using a SEIR-based mathematical model, augmented by optimal control theory. The model incorporates epidemiological data from the second wave of COVID-19 in Malaysia, considering critical parameters such as the transmission rate (β = 8.00373) and latency period (α = 0.175), including the key public health interventions, derived through curve fitting to the observed data. The model revealed that the nationwide Movement Control Orders (MCO) had led to a substantial reduction in the contact rate (?) from 1.0 to about 0.3, self-protection measures including the use of face masks further reduced transmissibility by 0.01507, with screening and contact tracing rates exceeding 90% while case detection rate was about 0.3. To optimize intervention strategies, optimal control theory was employed, aiming to balance the effectiveness of controlling the virus with minimizing the socio-economic costs of these interventions. Several simulations were conducted to explore the impact of varying control measures, including enhanced screening and contact tracing, case detection, movement control, and self-protection. Simulations revealed that movement control was most effective in the early stages but should not be prolonged to minimize economic disruptions. Meanwhile, simulations also underscored the importance of self-protection, enhanced screening and contact tracing, and case detection, highlighting their critical role in long-term pandemic control. Results from the simulations also emphasized the importance of flexible, adaptive intervention strategies that can be tailored to the evolving situation. This study highlights the utility of mathematical modelling in understanding COVID-19 transmission and guiding public health responses. The findings from this study provide valuable insights into the optimal allocation of resources and the design of adaptive strategies to control COVID-19 and future pandemics. The use of optimal control modeling in this context highlights its utility for guiding evidence-based decision-making, offering a framework for improving pandemic preparedness and response.