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

Abstract

IntroductionThe 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.MethodologyWe 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™.ResultsAt 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 September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (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 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.Discussion and ConclusionBoth 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.

Spatial Analysis to Identify Hotspots of Covid-19 Incidence in Jambi Municipality Indonesia

“…Cold spots are consistently found in Central Jakarta, and Hotspots are consistently found in South Jakarta (Syetiawan et al, 2022). A study conducted in Malaysia found that there was a clustering of Covid cases in certain areas (Cheong et al, 2022). Another study also conducted in Malaysia found a relationship between population density and the incidence rate of Covid-19 cases (Ganasegeran et al, 2021).…”

Geo-visualization of Sarawak COVID-19 Publicly Available Data Employing Open-source Geospatial Software

“…Although several spatiotemporal modeling on district-wise aggregated COVID-19 data were carried out in Malaysia [18][19][20] and specifically for Sarawak [21], to our best knowledge, no such geo-visualization and geospatial analysis were performed for COVID-19 exposed location point data within a division in Sarawak. Motivated by several studies on examining the distribution of the contagion by COVID-19 in urban environments of a city in Spain [22] and China [23] using authentic but anonymized microdata of infected people, this paper illustrates the utility of QGIS on geospatial visualization and analysis for the communication of publicly available COVID-19 exposed location data in Kuching, Sarawak.…”