Forecasting Public Health Risks in Malaysia: Insights from SEIR Modelling of Infectious Diseases

Nuha Rahim; Sakinah Ali Pitchay; M. A.B. Sahbudin; A.H. Azni; Ilfita K. Sahbudin

doi:10.33102/mjosht.v11i1.460

Authors

Nuha Rahim Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800, Nilai, Negeri Sembilan, Malaysia. https://orcid.org/0009-0004-7511-9206
Sakinah Ali Pitchay Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800, Nilai, Negeri Sembilan, Malaysia.
M. A.B. Sahbudin Institute of Applied Data Analytics (IADA), Universiti Brunei Darussalam, Brunei.
A.H. Azni CyberSecurity and Systems (CSS) Research Unit, Universiti Sains Islam Malaysia, 71800, Nilai, Negeri Sembilan, Malaysia.
Ilfita K. Sahbudin Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, United Kingdom.

DOI:

https://doi.org/10.33102/mjosht.v11i1.460

Keywords:

Public Health, Infectious Disease, Disease Forecasting, COVID-19, Healthcare

Abstract

During the global pandemic of 2020, the Malaysian government implemented a Movement Control Order (MCO) as a mitigation plan to control the spreading of COVID-19. This effort demonstrates a significant decrease in active cases. However, it is disturbing the nation economically. The lack of a visualization hub to predict the spread of infectious diseases in Malaysia disrupts decision-makers’ efficiency in optimizing health sectors. Therefore, this paper aims to forecast infectious diseases in Malaysia using the Susceptible, Exposed, Infectious, Removed (SEIR) model. The SEIR Model predicts the projection of disease-spread cases based on the sample of previous cases. The samples used public health data from the COVID-19 Open Data Repository, which covers 12 states in Malaysia. The finding revealed that the SEIR model demonstrates a sharp decline in susceptible individuals after three months into 2022, a peak in exposed and infectious individuals around the same time, and a steady rise in recovered individuals as most of the population becomes immune. This forecast data can provide earlier insights on the infection trend, allowing actionable recommendations for policymakers and healthcare.

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