Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The Pandemic

Parichat Wetchayont,Katawut Waiyasusri

doi:10.24057/2071-9388-2021-090

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.

Highlights

The infection rate first increased in Bangkok, Chonburi and Prachuap Khiri Khan Provinces, came firstorder neighboring provinces like Nonthaburi, Pathum Thani, Samutprakarn, and Phetchaburi (Figure 5b), and eventually second-order neighboring provinces such as Nakhon Pathom, Chachoengsao and Samut Songkhram (Figure 5c)
A PySAL package based on python language was used to detect early transmission by using the spatial distribution of COVID-19 incidence in Thailand’s provinces and its relationship with sociodemographic factors in order to better understand the outbreak and transmission of the disease in Thailand from April to July 2021
Global spatial autocorrelation was employed to confirm that COVID-19 incidence in Thailand has a spatial association, and the correlation characteristics increased at a slow rate in April, and rapidly increased in June and July 2021

Summary

Objectives

The goal of this study is to detect early transmission by using spatial distribution of COVID-19 incidence in Thailand’s provinces and its relationship with sociodemographic factors. The goal of this research was to examine if COVID-19 incidences in Thailand had a spatial correlation

Methods

Results

Discussion

Conclusion