Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics

Hoang, Anh-huy; Nguyen, Tien-thanh

doi:10.4018/ijagr.297517

Cited by 11 publications

(23 citation statements)

References 41 publications

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“…The results showed that seven hotspots of COVID-19 cases in provinces were detected in areas of high population density in the north-eastern region of Vietnam (Figure 1). Also in Vietnam, the local Moran's I spatial statistic and Moran scatterplot were successfully employed to identify highhigh and low-low clusters and low-high and high-low outliers of COVID-19 cases from a dataset of 10,742 locally transmitted cases in four COVID-19 waves in 63 prefecture-level cities/provinces in Vietnam (16). A Moran's I autocorrelation and spatial cluster analysis for identifying Coronavirus disease COVID-19 using GIS approach was also successfully carried out in Iraq (17).…”

Section: Results and Discussion 31 Roles Of Geographic Information Sy...mentioning

confidence: 99%

The Crucial Roles of Geospatial Techniques in The COVID-19 Fight : A Systematic Review

Nguyen,,

Giang Nguyen,

Mai Nguyen

2023

IJHMR

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The Corona Virus Disease 2019 (COVID-19) appeared in Wuhan, China, at the end of 2019, spreading from there across China and the whole world. Such advanced technologies as geospatial techniques have been applied to fight the rapid spread of SARS-CoV-2. This paper aims to review and synthesize the types of applications offered through geospatial techniques to help address different issues related to the fight of the COVID-19 pandemic. The content is presented under four sub-sections; namely the roles of GIS, Remote Sensing, Global Positioning System and Internet Mapping Technologies in the fight of the COVID-19 pandemic. It was found from summarising 73 scientific papers, geospatial techniques have been proven their effectiveness not only in the study of COVID-19 in general, but also in the fight of the pandemic in particular. The wide range of applications offered by geospatial techniques affirms the value of this technique to the COVID-19 fight.

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Section: Results and Discussion 31 Roles Of Geographic Information Sy...mentioning

confidence: 99%

The Crucial Roles of Geospatial Techniques in The COVID-19 Fight : A Systematic Review

Nguyen,,

Giang Nguyen,

Mai Nguyen

2023

IJHMR

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“…Hotspot can separate clusters of high values from cluster of low values. It is, therefore, Getis-Ord's G i * statistic was used to identify the counties of high and low numbers of COVID-19 cases (29,30). The form of Getis-Ord's G i * statistic is defined as follows (31):…”

Section: Getis Ord 𝐆 𝐢 * Statistic-based Hotspot Analysismentioning

confidence: 99%

Detection Of Hotspots Of Tuberculosis Cases In Vietnam

Nguyen,

Luong

2024

IJHMR

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Background: Tuberculosis (TB) is regarded as one of the leading causes of death globally. It remains a significant cause of morbidity and mortality in Vietnam. This study aims to identify hotspots of TB using boxplot and Getis-Ord’s G_i^* statistic-based hotspot analysis. Data used and Methods: A total of 101,438 TB cases in 2020 collected from 63 provinces/cities in Vietnam was used in study. Boxplot is first used to study distribution of TB cases. Getis-Ord’s G_i^* statistic was then employed to identify hotspots of TB cases. Finally, results and main findings will be discussed and concluded. Results: It was found that a total of 05 hotspots and 04 coldspots of TB cases were detected throughout Vietnam. Five hotspots were detected in 05 provinces in the northeastern region including Ha Nam, Nam Dinh, Hai Phong, Hai Duong, and Hung Yen. Whereas, four coldspots were mainly concentrated in 03 provinces in the northwest region (Cao Bang, Tuyen Quang and Son La), and Dak Lak in the central south region. Conclusion: It can be concluded that the combination of boxplot and Getis-Ord’s G_i^* statistic can help to effectively detect hotspots of TB cases. Findings in this study provide an insight into how to used spatial statistics and spatial analysis in the study of TB distribution.

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“…Hotspot can separate clusters of high values from cluster of low values. It is, therefore, Getis-Ord's 𝐺 𝑖 * statistic was used to identify the counties of high and low numbers of COVID-19 cases (45,46). The form of Getis-Ord's 𝐺 𝑖 * statistic is defined as follows (47):…”

Section: Hotspot Analysismentioning

confidence: 99%

“…The Getis-Ord's 𝐺 𝑖 * coefficient at county i (𝐺 𝑖 * ) also ranges between -1 and +1. If 𝐺 𝑖 * > 0 and p(𝐺 𝑖 * ) < α then there exists a spatial clustering of high-high values (45,46). In this case, these high-high values, so-called a hotspot, reflects the presence of high numbers of COVID-19 cases among county i and its neighborhood counties (𝑗 ∈ 𝐽 𝑖 ).…”

Section: Hotspot Analysismentioning

confidence: 99%

“…In this case, these high-high values, so-called a hotspot, reflects the presence of high numbers of COVID-19 cases among county i and its neighborhood counties (𝑗 ∈ 𝐽 𝑖 ). Whereas, if 𝐺 𝑖 * < 0 and p(𝐺 𝑖 * ) < α then there exists a spatial clustering of low-low values (45,46). These low-low values are called a coldspots indicating low numbers of COVID-19 cases among county i and its neighborhood counties (𝑗 ∈ 𝐽 𝑖 ).…”

Section: Hotspot Analysismentioning

confidence: 99%

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An Assessment of the Relationship Between Population Density and the Incidence of Dengue Hemorrhagic Fever

Nguyen,

Nguyen

2023

Int J Sci Healthcare Res

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Background: Dengue is a leading cause of severe illness and hospitalization in Vietnam. This study aims to assess the relationship between population density and dengue hemorrhagic fever (DHF) in August 2023 in Ho Chi Minh city, Vietnam. Methods: Exploratory data analysis (EDA) techniques were first applied to investigate the distribution patterns of DHF incidence. It will go on to use Getis Ord statistic to detect hotspots and coldspots of DHF infection rates. A scatter plot will be then employed to quantitatively assess the relationship between population density and DHF infection rates and Getis Ord statistic. Finally, a dataset of DHF cases reported in August 2023 in Ho Chi Minh city was used to illustrate the proposed methods. Results: It was found that high values of Getis Ord coefficients was mainly concentrated in the east and west of the city. There was a negative correlation between population density and both variables (DHF infection rates and Getis Ord statistic). The population density likely decreases dengue hemorrhagic fever infection rates in August in Ho Chi Minh city. Conclusions: It can be concluded that spatial statistics and EDA have proved their effectiveness in the assessment of the relationship between population density and the incidence of dengue hemorrhagic fever. Findings of this study provide an insight into the effects of population density on DHF infection rates. Keywords: Relationship assessment, Population density, Spatial autocorrelation analysis, Dengue hemorrhagic fever, Infection rate, Ho Chi Minh city.

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Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics

Cited by 11 publications

References 41 publications

The Crucial Roles of Geospatial Techniques in The COVID-19 Fight : A Systematic Review

The Crucial Roles of Geospatial Techniques in The COVID-19 Fight : A Systematic Review

Detection Of Hotspots Of Tuberculosis Cases In Vietnam

An Assessment of the Relationship Between Population Density and the Incidence of Dengue Hemorrhagic Fever

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