ArticleFrontiers in public health2022
Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparative analysis of epidemiological and Spatiotemporal patterns in seasonal influenza and COVID-19 outbreaks.Scientific reports · 2025Article
- Spatio-temporal clustering analysis of COVID-19 cases in Johor.Infectious Disease Modelling · 2024Article
- GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China.Frontiers in public health · 2024Article
- Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia.BMC public health · 2023Article
- Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia.Scientific reports · 2023Article
- Potential Epidemic Vulnerability and Socioepidemiological Profile of SARS-CoV2 in the Brazilian Northeast Region.Tropical medicine and infectious disease · 2023Article
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9 authors.
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Abstract
Introduction: The unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission. Methodology: We obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™. Results: At the initial stage of the outbreak, Moran's I index > 0.5 ( Discussion and Conclusion: Both analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.
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