Publication: USING MORAN’S I FOR DETECTION AND MONITORING OF THE COVID-19 SPREADING STAGE IN THAILAND DURING THE THIRD WAVE OF THE PANDEMIC
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Issued Date
2021
Resource Type
Language
eng
File Type
application/pdf
ISSN
20719388
Other identifier(s)
2-s2.0-85124178120
Rights Holder(s)
Scopus
Bibliographic Citation
Geography, Environment, Sustainability. Vol 14, No.4 (2021), p.155-167
Suggested Citation
Wetchayont P., Waiyasusri K. USING MORAN’S I FOR DETECTION AND MONITORING OF THE COVID-19 SPREADING STAGE IN THAILAND DURING THE THIRD WAVE OF THE PANDEMIC. Geography, Environment, Sustainability. Vol 14, No.4 (2021), p.155-167. doi:10.24057/2071-9388-2021-090 Retrieved from: https://hdl.handle.net/20.500.14740/7912
Author(s)
Abstract
Spatial distribution and spreading patterns of COVID-19 in Thailand were investigated in this study for the 1 April – 23 July 2021 period by analyzing COVID-19 incidence’s spatial autocorrelation and clustering patterns in connection to population density, adult population, mean income, hospital beds, doctors and nurses. Clustering analysis indicated that Bangkok is a significant hotspot for incidence rates, whereas other cities across the region have been less affected. Bivariate Moran’s I showed a low relationship between COVID-19 incidences and the number of adults (Moran’s I = 0.1023-0.1985), whereas a strong positive relationship was found between COVID-19 incidences and population density (Moran’s I = 0.2776-0.6022). Moreover, the difference Moran’s I value in each parameter demonstrated the transmission level of infectious COVID-19, particularly in the Early (first phase) and Spreading stages (second and third phases). Spatial association in the early stage of the COVID-19 outbreak in Thailand was measured in this study, which is described as a spatio-temporal pattern. The results showed that all of the models indicate a significant positive spatial association of COVID-19 infections from around 10 April 2021. To avoid an exponential spread over Thailand, it was important to detect the spatial spread in the early stages. Finally, these findings could be used to create monitoring tools and policy prevention planning in future. © 2021, Russian Geographical Society. All rights reserved.