Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

Abstract

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

Spatial Clustering Analysis Of Dengue Hemorrhagic Fever In The First 9-Monthsof 2023 In Ho Chi Minh City, Vietnam

“…(1) where x i and x j are the dengue hemorrhagic fever incidence for district i and district j; x ̅ is the mean of the dengue hemorrhagic fever incidence and be given by x ̅ = ∑ x i n n i=1 ; n is the total number of districts in the whole study area; and W ij is a (n × n) spatial weight matrix (26). The range of values of global Moran's I coefficient is in the interval [-1, +1] (26).…”

“…The global Moran's I reflects the presence or lack of spatial autocorrelation as a whole. The regional Moran's I statistic was used to quantify the spatial clustering of low and high dengue hemorrhagic fever incidence in each district (26). The local Moran's I statistic (I i ) of dengue hemorrhagic fever incidence at district i is given by the following equation ( 28):…”

“…There is no spatial autocorrelation of dengue hemorrhagic fever incidence if the local Moran's I coefficient at district i equals zero (I i = 0). If I i > 0 then there will be a positive spatial autocorrelation of dengue hemorrhagic fever incidence (26). If I i < 0 then there will be a negative spatial autocorrelation of dengue hemorrhagic fever incidence.…”

Introduction To Geography Of Covid-19 Pandemic: Environmental Issues, Public Health And Socio-Economic Consequences

“…The problems concerning socio-spatial and geographical analysis of COVID-19 are discussed in the papers (Kolosov et al 2021;Kieu et al 2021;Rahardjo et al 2021;Sahu and Mishra 2021;Vu et al 2021;Talib 2021;Wetchayont and Waiyasusri 2021). Kolosov et al (2021, this issue) reviewed the main areas and methods of human-geographical studies.…”

COVID-19 Infection: The Virus and Its Origin, the Variants, the Immune Defense, the Multiorgan Autoimmune Reactions, and the Targeted Treatments

“…Vietnam is no exception. From July to October 16, 2021, despite the same anti-pandemic effort, the more virulent Delta SARS-CoV-2 took over 21,000 lives among 913,400 infected patients in the country's fourth wave of COVID-19 with an IMR of about 2.32%, compared to about and less than 0.43% in the first three waves [41] [49] [50] [51]. The fifth wave, still with Delta variant (second Delta variant infection wave), from October 2021 to the end of January 2022, when Vietnam reopened with the idea "to live with COVID", saw a larger amount of infection likely due to low number of vaccinations (Figure 1).…”