Regional and Demographic Patterns of Covid-19 in Malaysia: A State and Federal Territory Analysis of Cases, Mortality, Age and Vaccination Status, 2020–2025

This study analyses the burden of COVID-19 in Malaysia at the regional and demographic levels based on an aggregated dataset at the state level comprising 16 states and federal territories. Analysis is based on reported cases, reported deaths, age group composition, vaccination status, and will attempt to place the COVID-19 surveillance data in a population and social-science context. Descriptive statistics were used to compare burden on a state-by-state basis, to calculate crude case-fatality ratios, and to look at characteristics of cases and deaths by vaccination status and age. There were 5,330,305 reported case and 37,351 deaths reported. Selangor reported the highest number of cases, 30.69%, and deaths, 29.63%, while the five largest state level contributors together reported 62.42% of the cases. For cases with known age, adults accounted for 71.62%, the children for 12.36%, the adolescents for 6.31% and elderly for 9.71%. When broken down by vaccination status, 38.41% of reported cases were unvaccinated, 8.20% were partially vaccinated, 26.67% were fully vaccinated and 26.72% had been boosted. On the other hand, 60.34% of reported deaths were among unvaccinated individuals; 16.91% among those who were partially vaccinated; 18.74% among those fully and 4.01% among those who were boosted. The results indicate significant geographic and demographic variation in the reported burden of COVID-19 in Malaysia. The difference in the vaccination status of cases and deaths indicates that vaccination status is an important factor to consider in pandemic analysis at the population level, where vaccination status cannot be used to determine person-to-person vaccine effectiveness or cause-and-effect. The study calls for geographically-based health communications, for targeted age-based protection and for the maintenance of clear disaggregated surveillance data in future public-health crises.

COVID-19; Malaysia; regional inequality; vaccination status; population studies

1. Cheong, Y. L., Ghazali, S. M., Che Ibrahim, M. K., Kee, C. C., Md Iderus, N. H., Ruslan, Q., Gill, B. S., Lee, F. C. H., & Lim, K. H. (2022). Assessing the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia. Frontiers in Public Health, 10, 836358. https://doi.org/10.3389/fpubh.2022.836358 [Google Scholar] [Crossref]

2. Ganasegeran, K., Jamil, M. F. A., Ch'ng, A. S. H., Looi, I., & Peariasamy, K. M. (2021). Influence of population density for COVID-19 spread in Malaysia: An ecological study. International Journal of Environmental Research and Public Health, 18(18), 9866. https://doi.org/10.3390/ijerph18189866 [Google Scholar] [Crossref]

3. Jayaraj, V. J., Rampal, S., Ng, C.-W., & Chong, D. W. Q. (2021). The epidemiology of COVID-19 in Malaysia. The Lancet Regional Health – Western Pacific, 17, 100295. [Google Scholar] [Crossref]

4. https://doi.org/10.1016/j.lanwpc.2021.100295 [Google Scholar] [Crossref]

5. Lim, P. Y., Md Said, S., Kadir Shahar, H., Azman, A. Z. F., Mokhtar, S. A., & Mahmud, A. (2022). COVID-19 inpatient deaths and brought-in-dead cases in Malaysia. Frontiers in Public Health, 10, 872838. https://doi.org/10.3389/fpubh.2022.872838 [Google Scholar] [Crossref]

6. Ministry of Health Malaysia. (2025). Daily COVID-19 cases by age group & state. KKMNOW. https://data.moh.gov.my/ms-MY/data-catalogue/covid_cases_age [Google Scholar] [Crossref]

7. Ministry of Health Malaysia. (2025). Daily COVID-19 cases by state. KKMNOW. https://data.moh.gov.my/data-catalogue/covid_cases [Google Scholar] [Crossref]

8. Ministry of Health Malaysia. (2025). Daily COVID-19 cases by vaccination status & state. KKMNOW. https://data.moh.gov.my/data-catalogue/covid_cases_vaxstatus [Google Scholar] [Crossref]

9. Syed Alwi, S. A. R., Rafidah, E., Zurraini, A., Juslina, O., Brohi, I. B., & Lukas, S. (2021). A survey on COVID-19 vaccine acceptance and concern among Malaysians. BMC Public Health, 21, 1129. [Google Scholar] [Crossref]

10. https://doi.org/10.1186/s12889-021-11071-6 [Google Scholar] [Crossref]

11. Teh, J. K. L., Teo, K. L., Bradley, D. A., Chook, J. B., Ang, W. T., & Peh, S.-C. (2024). State-level differentials in COVID-19 fatality: Exploring age and sex disparities in Malaysia’s pandemic experience. Discover Social Science & Health, 4, 9. https://doi.org/10.1007/s44155-024-00068-2 [Google Scholar] [Crossref]

12. Vaithilingam, S., Hwang, L.-A., Nair, M., Ng, J. W. J., Ahmed, P., & Musa, K. I. (2023). COVID-19 vaccine hesitancy and its drivers: An empirical study of the vaccine hesitant group in Malaysia. PLOS ONE, 18(3), e0282520. https://doi.org/10.1371/journal.pone.0282520 [Google Scholar] [Crossref]

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