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

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.

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Clustering Analysis Influenza Disease to Identify Spatio-Temporal Spread Pattern in Thailand

International Journal of Geoinformatics ◽

10.52939/ijg.v17i5.2015 ◽

2021 ◽

pp. 81-89

Keyword(s):

Population Density ◽

Clustering Analysis ◽

Statistical Significance ◽

Winter Season ◽

Moran’S I ◽

P Value ◽

Moran's I ◽

Spread Pattern ◽

Spatio Temporal ◽

Influenza Outbreaks

The pandemics of influenza in Nonthaburi province was investigated by using autoregression and found the influenza spread pattern by autocorrelation (Moran's I). Population density, temperature, relative humidity, and rainfall are the factors used in the analysis. The influenza quantitative cross-section retrospective research design was employed from 2003-2010. Three seasons are classified as: hot, rainy, and winter season. The study found that influenza outbreaks in the rainy season was R2=0.45 and population density apparently affected the spread of influenza incidence with statistical significance coefficient (p-value <0.05). From the distribution pattern, the highest Moran's I values were related with the highest population density in 4 sub-districts: Suenyai, Taladkhwun, Bangkhen, and Bangkruay sub-district.

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Moran's I statistic-based nonparametric test with spatio-temporal observations

Journal of Nonparametric Statistics ◽

10.1080/10485252.2018.1550197 ◽

2018 ◽

Vol 31 (1) ◽

pp. 244-267 ◽

Cited By ~ 2

Author(s):

Y. Xiong ◽

D. Bingham ◽

W. J. Braun ◽

X. J. Hu

Keyword(s):

Nonparametric Test ◽

Moran’S I ◽

Moran's I ◽

Spatio Temporal

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ANALISIS PEMANFAATAN KAWASAN WILAYAH PESISIR MENGGUNAKAN LOCAL INDICATORS OF SPATIAL ASSOCIATION (LISA) (STUDI KASUS : KABUPATEN KULON PROGO)

Simetris Jurnal Teknik Mesin Elektro dan Ilmu Komputer ◽

10.24176/simet.v8i2.1608 ◽

2017 ◽

Vol 8 (2) ◽

pp. 781

Author(s):

Tirsa Ninia Lina ◽

Eko Sediyono ◽

Sri Yulianto Joko Prasetyo

Keyword(s):

Spatial Association ◽

Moran’S I ◽

Moran's I

Kawasan pesisir Kabupaten Kulon Progo terdiri dari empat kecamatan, yaitu kecamatan Galur, Panjatan, Wates, dan Temon. Kawasan pesisir ini rentan terhadap dampak negatif aktifitas manusia seperti penggunaan tanah atau pemanfaatannya yang sering tumpang tindih. Tujuan penelitian ini untuk menganalisis autokorelasi spasial terhadap pemanfaatan kawasan wilayah pesisir di Kabupaten Kulon Progo. Penelitian ini menggunakan salah satu pengujian autokorelasi spasial yaitu Local Indicators of Spatial Association (LISA) dengan indikator Local Moran's I, yang menghasilkan signifikansi secara statistik tinggi (hotspots), signifikansi secara statistik rendah (coldspots), dan pencilan (outlier). Hasil dari penelitian ini menunjukkan bahwa kecamatan yang termasuk kategori hotspots (H-H) diantaranya Temon dengan lima hotspots pada kawasan permukiman perdesaan, pertanian lahan kering, industri, sempadan pantai, dan suaka alam, Panjatan dengan tiga hotspots pada kawasan permukiman perkotaan, perdagangan, dan sempadan sungai, Galur dengan dua hotspots pada kawasan pertanian lahan basah dan perdagangan, dan Wates dengan satu hotspots pada kawasan industri.Kata kunci: kawasan pesisir, Kabupaten Kulon Progo, Local Indicators of Spatial Association, LISA, Local Moran's I.

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Analysis on Spatial Difference of the Rural Resident’s per Capita Net Income in Qinhuangdao City Based on ESDA

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.3893 ◽

2014 ◽

Vol 955-959 ◽

pp. 3893-3898

Author(s):

Yu Hong Wu

Keyword(s):

Spatial Data ◽

Rural Economy ◽

Spatial Association ◽

Spatial Data Analysis ◽

Moran’S I ◽

Net Income ◽

Gis Technology ◽

Moran's I ◽

Per Capita ◽

Global Moran’S I

Based on the exploratory spatial data analysis (ESDA) and GIS technology, the spatial differences of the rural economic development level of Qinhuangdao city was investigated by adopting the rural resident’s per capita net income data at town level in Qinhuangdao city from 2007 to 2011. The results of global Moran’s I value for rural resident’s per capita net income at town level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident’s per capita net income. However, the global Moran’s I value showed a decreasing trend during 2007 to 2011, indicating an enlarged spatial disparity of rural economy at the town level. The results of the Moran scatter plots and LISA cluster maps of 2007 and 2011 showed that most of towns were characterized by positive local spatial association , ie. They were located in the HH or the LL quadrant. The significant HH towns were mostly to be found in the south of Qinhuangdao city, Haigang district, Changli county, Lulong county. The significant LL towns were mostly to be found in the Qinglong county, north of Qinhuangdao city.

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SPATIAL ASSOCIATION PATTERNS OF SMOKING, TOBACCO OUTLET DENSITY, AND SECONDHAND SMOKE IN THAILAND

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.56.5.30 ◽

2021 ◽

Vol 56 (5) ◽

pp. 351-361

Author(s):

Pittaya Thammawongsa ◽

Wongsa Laohasiriwong ◽

Nakarin Prasit ◽

Surachai Phimha

Keyword(s):

Spatial Autocorrelation ◽

Secondhand Smoke ◽

Spatial Regression ◽

Spatial Association ◽

Moran’S I ◽

Data Set ◽

Moran's I ◽

High Prevalence ◽

Smoking Behaviors ◽

Outlet Density

Thailand has a higher prevalence of smoking behaviors which puts people at risk of morbidity and mortality. This study aimed to determine the spatial association of smoking behaviors and their associated factors among the population of Thailand. This study was conducted using a data set from the National Statistical Office of Thailand, 2017. A Moran’s I, local indicators of spatial association (LISA), and spatial regression were used to identify the spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors among Thai people. According to the results, among 88,689 participants, the prevalence of smoking behaviors was 18.00 per 1,000 population. There was global spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors with the Moran’s I values of 0.120 and 0.375, respectively. The LISA analysis identified significant positive spatial local autocorrelation of smoking behaviors in the form of nine high-high clusters of tobacco outlets density and ten high-high prevalence clusters of secondhand smoke. The prevalence of secondhand smoke predicted smoking behaviors by 62.8 percent. There were spatial associations between tobacco outlet density and secondhand smoke problems that led the youngsters to start smoking. It is a general recommendation to strictly enforce policies and laws to control smoking, and cover all regions in Thailand.

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What do we know about the behavior of animal rabies in Chile through the last years?

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7757 ◽

2017 ◽

Vol 9 (1) ◽

Author(s):

Raul Alegria-Moran ◽

Daniela Miranda ◽

Alonso Parra ◽

Lisette Lapierre

Keyword(s):

Spatial Autocorrelation ◽

Spatial Association ◽

Winter Season ◽

Central Zone ◽

Domestic Dog ◽

Moran’S I ◽

P Value ◽

Moran's I ◽

Epidemiological Characterization

ObjectiveThis study aims to analyze the evolution of the epidemiologicalbehavior of rabies in Chile during the period 2003 to 2013, throughthe epidemiological characterization of a number of variables anddescription of spatial and temporal patterns of animal cases.IntroductionRabies is a zoonotic disease caused by an RNA virus from thefamily Rhabdoviridae, genus Lyssavirus. Worldwide distributed,control of rabies has been considered to be particularly amenable toa “One Health” strategy (1). In Chile, rabies was considered endemicin domestic dog population until the late 1960s, when a surveillanceprogram was established, decreasing the number of human casesrelated to canine variants until the year 1972 (2). Rabies is recognizedas a endemic infection in chiropterans of Chile and prompted thesurveillance of the agent in this and other species (3).MethodsAn epidemiological characterization of the registered cases fromthe National Program for Prevention and Control of Rabies wascarried. During the period 2003-2013, 927 cases were reported.Descriptive statistics and descriptive mapping, recording origin of thesample, number of cases per region, animal reservoir implicated andviral variant were performed. A spatial autocorrelation analysis wascarried using Moran’s I indicator for the detection of spatial clusters(4), using the Local Indicators of Spatial Association (LISA) statistics(5), at national and regional level of aggrupation (north, central andsouth zone). Temporal descriptive analysis was carried.Results927 positive cases were recorded. 920 (99.2%) cases came frompassive surveillance, while 7 (0.8%) cases by active surveillance, totalpositivity was 77.02% and 1.37% respectively. Positivity was reportedmainly in the central zone (88.1%), mainly in Valparaiso (19.1%),Metropolitana (40.6%) (Figure 1), Maule (11.8%) regions concentratedin urban centers. Main positive reservoirs were bats (99.8%),specificallyTadarida brasiliensisand viral variant 4 was the mostcommonly diagnosed. LISA test gives a Moran’s I indicator of 0.1537(p-value = 0.02) for the central zone (Table 1). Rabies tend to decreasein fall and winter season (2.9 cases vs 13 cases during summer).ConclusionsWildlife rabies in bats remains endemic in Chile, concentrated inurban areas. The main reservoirs are insectivorous bats. There is asignificant spatial autocorrelation of animal rabies cases in the centralzone of Chile. Results are relevant to the design of preventive andcontrol measures.

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Spatial Analysis to Mitigate the Spread of Covid-19 Based on Regional Demographic Characteristics

Forum Geografi ◽

10.23917/forgeo.v35i1.12325 ◽

2021 ◽

Vol 35 (1) ◽

Author(s):

Mochamad Firman Ghazali ◽

Anggun Tridawati ◽

Mamad Sugandi ◽

Aqilla Fitdhea Anesta ◽

Ketut Wikantika

Keyword(s):

Surface Temperature ◽

Satellite Imagery ◽

Elderly Population ◽

Spatial Association ◽

The Elderly ◽

Demographic Characteristics ◽

Moran’S I ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Moran's I

COVID-19 is currently the hot topic of discussion by scientists because of its ability to quickly spread, in line with everyday human activities. One of the environmental factors related to climatic parameters, such as the air temperature, contributed to the spreading of COVID-19 in the last four months. Its distribution ability is no longer local as it successfully halts the important activities in many countries globally. This study aims to explain the opportunity of geospatial analysis in handling the COVID-19 distribution locally based on the characteristics of demographic data. Various data, including the confirmed positive for COVID-19, age-based population, and Landsat 8 satellite imagery data were used to determine the spatial characteristics of the COVID-19 distribution per September 2020 in Bandung, Indonesia. An inverse distance weighted (IDW), Moran's I index and local indicator spatial association (LISA), and a proposed ratio of the elderly population against the population with confirmed positive for COVID-19 (CoVE) were used as the approach to determine its distribution characteristics. The information derived from Landsat 8 satellite imagery, such as the residential area, surface temperature, and humidity based on the supervised classification, land surface temperature (LST), and the normalized difference water index (NDWI) was used to perform the analysis. The results showed that the positive population of COVID-19 was concentrated in Bandung city. However, with a Moran's I value of 0.316, not all are grouped into the same category. There are only 8, 2, 5, and 3 districts categorized as HH, HL, LL, and LH. However, the areas with a large or small number of elderlies do not always correlate with the high number of confirmed positives for COVID-19. There are only 3, 1, and 3 districts classified as HH, HL, and LL. They were represented by the values of Moran's I, for about 0.057. The positive relationship between confirmed positive for COVID-19 and the built-up area, surface temperature, humidity, and the elderly population based on the coefficient of determination (R2) were 0.03, 0.28, 0.25, and 0.019, respectively. The study also shows that the vulnerability of those areas is relatively low. The study shows that the vulnerabilities in these areas are relatively low and the recommendation for COVID-19 widespread mitigation has to consider the demographic characteristics precisely in the large scale social restrictions (LSSR).

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SPATIAL ASSOCIATION PATTERNS OF BINGE DRINKING, ALCOHOL OUTLET DENSITY, AND EARLY STARTED DRINKING IN THAILAND

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.56.4.16 ◽

2021 ◽

Vol 56 (4) ◽

pp. 187-198

Author(s):

Nakarin Prasit ◽

Wongsa Laohasiriwong ◽

Kittipong Sornlorm ◽

Surachai Pimha

Keyword(s):

Spatial Autocorrelation ◽

Binge Drinking ◽

Spatial Association ◽

Moran’S I ◽

Alcohol Outlet ◽

Data Set ◽

Moran's I ◽

Alcohol Outlets ◽

Alcohol Outlet Density ◽

Outlet Density

Thailand had a higher prevalence of binge drinking (BD) behaviors which put them at risks of morbidity and mortality. This study aimed to determine the spatial association of BD and its associated factors among the population of Thailand. This study was conducted using a data set of the National Statistical Office of Thailand and another data set of the Center for Alcohol Studies, Thailand, in 2017. A Moran's I, Local Indicators of Spatial Association (LISA), and Spatial regression were used to identify the spatial autocorrelation between alcohol outlet density, started drinking before 20 years of age, and BD among Thai people. According to the results, among 61,708 participants, the prevalence of BD was 11.47 per 1,000 population. There was global spatial autocorrelation between alcohol outlet density, start drinking before 20 years, and BD with the Moran's I values of 0.10 and 0.54, respectively. The LISA analysis identified significant positive spatial local autocorrelation of BD in the form of two high-high clusters for density of alcohol outlets and seven high-high clusters of started drinking before the age of 20. Started drinking before 20 years of age could predict binge drinking behaviors by 62.8 percent. There were spatial associations between alcohol outlet density and problems with alcoholic beverage control law enforcement that let the youngsters start drinking before 20. It is a general recommendation to strictly enforce the law in prohibited the underage from consuming alcohol, especially in the high density of alcohol outlets.

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Spatial autocorrelation analysis in plant population: An overview

Journal of Applied and Natural Science ◽

10.31018/jans.v7i1.639 ◽

2015 ◽

Vol 7 (1) ◽

pp. 501-513 ◽

Cited By ~ 10

Author(s):

Manish Mathur

Keyword(s):

Spatial Autocorrelation ◽

Spatial Association ◽

Plant Population ◽

Moran’S I ◽

Moran's I ◽

Entire Study ◽

Cold Spots ◽

The Many ◽

Global Spatial Autocorrelation ◽

Entire Study Area

Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given.

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