Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has vastly impacted our national health and economic industries. Hence, the utilisation of big data simulation of the outbreak is essential to guide policymakers, government, and health authorities in better understanding the dynamics of the infectious disease. This paper integrates the Agent-Based-Model (ABM) and Susceptible, Exposed, Infectious and Recovered (SEIR) framework to understand the dynamic transmission of COVID-19 in Sabah, Malaysia. This study employed NetLogo software, which includes parameters such as geographical distribution, population density, variant type, lockdown measures, and vaccination rates across 27 districts, to run the simulation and assess the potential impact of public health interventions. The methodology involves different scenario simulations using varying variant types, vaccination coverage, lockdown, and social distancing measures to determine the virus transmission level. The results indicate that higher vaccination coverage and strict adherence to preventive measures can reduce the spread of the virus, especially in highly densely populated areas. Our simulation resulted in a 2.54% variance with the true data following the parameters and settings mentioned above. Additionally, this study also found that geographical structure and uneven distribution of healthcare across the Sabah district notably affect disease and disaster management and intervention policy and efficacy. These insights are crucial for Malaysian policymakers and health authorities, which need to tailor the public health responses considering geographical and demographic settings. Future recommendations include data of higher geographical resolution, immunisation records, and real-time mobility data to portray a more realistic simulation.

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