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 in...
| Main Authors: | , , , , , , |
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| Format: | Article |
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Universiti Putra Malaysia Press
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118742/ |
| _version_ | 1848867774770708480 |
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| author | Mohd Isnan, Siti Sarah Abdullah, Ahmad Fikri Mohamed Shariff, Abdul Rashid Ishak, Iskandar Syed Ismail, Sharifah Norkhadijah Tai, Doria Appanan, Maheshwara Rao |
| author_facet | Mohd Isnan, Siti Sarah Abdullah, Ahmad Fikri Mohamed Shariff, Abdul Rashid Ishak, Iskandar Syed Ismail, Sharifah Norkhadijah Tai, Doria Appanan, Maheshwara Rao |
| author_sort | Mohd Isnan, Siti Sarah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T14:41:51Z |
| format | Article |
| id | upm-118742 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:41:51Z |
| publishDate | 2025 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1187422025-07-23T04:28:57Z http://psasir.upm.edu.my/id/eprint/118742/ Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia Mohd Isnan, Siti Sarah Abdullah, Ahmad Fikri Mohamed Shariff, Abdul Rashid Ishak, Iskandar Syed Ismail, Sharifah Norkhadijah Tai, Doria Appanan, Maheshwara Rao 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. Universiti Putra Malaysia Press 2025 Article PeerReviewed Mohd Isnan, Siti Sarah and Abdullah, Ahmad Fikri and Mohamed Shariff, Abdul Rashid and Ishak, Iskandar and Syed Ismail, Sharifah Norkhadijah and Tai, Doria and Appanan, Maheshwara Rao (2025) Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia. Pertanika Journal of Science and Technology, 33 (2). pp. 653-676. ISSN 0128-7680; eISSN: 2231-8526 http://pertanika2.upm.edu.my/pjst/browse/prepress-issue?article=JST-5165-2024 10.47836/pjst.33.2.06 |
| spellingShingle | Mohd Isnan, Siti Sarah Abdullah, Ahmad Fikri Mohamed Shariff, Abdul Rashid Ishak, Iskandar Syed Ismail, Sharifah Norkhadijah Tai, Doria Appanan, Maheshwara Rao Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia |
| title | Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia |
| title_full | Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia |
| title_fullStr | Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia |
| title_full_unstemmed | Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia |
| title_short | Leveraging computational model approach in understanding infectious disease: a case study in Sabah, Malaysia |
| title_sort | leveraging computational model approach in understanding infectious disease: a case study in sabah, malaysia |
| url | http://psasir.upm.edu.my/id/eprint/118742/ http://psasir.upm.edu.my/id/eprint/118742/ http://psasir.upm.edu.my/id/eprint/118742/ |