AUTHOR=Cheong Yoon Ling , Ghazali Sumarni Mohd , Che Ibrahim Mohd Khairuddin bin , Kee Chee Cheong , Md Iderus Nuur Hafizah , Ruslan Qistina binti , Gill Balvinder Singh , Lee Florence Chi Hiong , Lim Kuang Hock 

TITLE=Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.836358

DOI=10.3389/fpubh.2022.836358

ISSN=2296-2565

ABSTRACT=Introduction: The unprecedented COVID-19 pandemic has greatly affected human health and socio-economic. 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 patterns 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 25 January 2020 to 24 February 2021, one day before national vaccination program was initiated. All analyses were based on the daily cumulated cases, derived from the sum of retrospective seven days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation, 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 (p<0.05) was observed. Local Moran’s I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on 1 September (Cumulative cases=9,354; Moran’s I=0.34; p<0.05), to 11 districts on 19 October (Cumulative cases=21,363, Moran’s I=0.52, p<0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR=11.93; p<0.001) which encompassed 36 districts with a radius of 178.8km, from 24 November 2020 to 24 February 2021, followed by the Sabah cluster. 
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 in 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.