The Risk Distribution of COVID-19 in Indonesia: A Spatial Analysis

Eryando, Tris; Sipahutar, Tiopan; Rahardiantoro, Septian

doi:10.1177/1010539520962940

Cited by 23 publications

(32 citation statements)

References 6 publications

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“…It can be seen that provinces in Java Island were clustered in Quandrant 1, which consisted of provinces with a high cumulative number of COVID-19 cases followed by high risk from the surrounding areas. This finding was different from the previous research which illustrated the situation on April 29 th , 2020 where the high-risked provinces were West Java, Jakarta Special Capital Region, Gorontalo, and South Sulawesi (11) . The density of the population and high mobility allowed COVID-19 to spread quickly.…”

Section: Figure 1 Moran's Scatterplot For the Cumulative Number Of Confirmed Covid-19-19 Cases In Indonesia Oncontrasting

confidence: 99%

“…Several studies about the distribution of COVID-19 also utilizing spatial autocorrelation. Research on the spatial modeling of COVID-19 had been carried out in Yogyakarta, Indonesia (10) , and the spread of COVID-19 in Indonesia in April 2020 (11) . Research on the distribution of COVID-19 with spatial statistics has also been carried out in several other countries, including China (12) , Kuwait (13) , South Korea (14) , Bangladesh (15) .…”

Section: Introductionmentioning

confidence: 99%

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The Distribution Pattern of Covid-19 in Indonesia: Study Using Spatial Autocorelation

Mahmudah,

Azizah,

Kusumaningtyas

et al. 2021

IJONE

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COVID-19 is an infectious disease caused by the coronavirus, SARS-CoV-2. The spread of several types of diseases, including COVID-19, is influenced by location or spatial factor. The purpose of this research was to examine the spatial relationship among the spread of COVID-19 in Indonesia. Secondary data is used from the Indonesian Ministry of Health website. The data analyzed was the cumulative number of positive cases of COVID-19 in Indonesia until October 19 th , 2020. Spatial Autocorrelation, Moran's Index, and Local indicators of spatial association (LISA) were used to examine these relationships. Geoda software was used in performing spatial statistical analysis. The spread of the cumulative number of COVID-19 cases in Indonesia is spatially related, with a Moran's Index value of 0.251. Four provinces have significant LISA scores, namely South Sumatra, Banten, West Java, and South Sulawesi. South Sumatera is relatively safe since it is located in the an area with relatively low COVID-19 cases. West Java is perilouse with high COVID-19 cases and surrounded by an area with a high number of cases. Banten needs to be vigilant of the high number of cases' possibility since the surrounded area can be the main influence. South Sulawesi has a very high number of COVID-19 cases, which can be the new source of infection for the surrounding areas. The Indonesian government needs to act on a regulation in order to decrease COVID-19 distribution by limiting people's commute between provinces.

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Section: Figure 1 Moran's Scatterplot For the Cumulative Number Of Confirmed Covid-19-19 Cases In Indonesia Oncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Distribution Pattern of Covid-19 in Indonesia: Study Using Spatial Autocorelation

Mahmudah,

Azizah,

Kusumaningtyas

et al. 2021

IJONE

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“…Spatial statistics and the modeling tool of ArcGIS 10.6 were used to test whether the confirmed provincial and municipal cases had significant global or local spatial autocorrelation. Spatial autocorrelation, which measures spatial autocorrelation based on feature locations and eigenvalues, can be divided into global spatial autocorrelation and local spatial autocorrelation ( Eryando et al, 2020 ). Use global Moran's I statistics to assess whether the cumulative number of confirmed COVID-19 cases in each region is spatially relevant.…”

Section: Methodsmentioning

confidence: 99%

Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

Wang¹,

Dong²,

Yang³

et al. 2021

International Journal of Infectious Diseases

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Objectives The purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China from January to October 2020. Methods About 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases. Results The distribution of COVID-19 in China tends to be stable over time, with spatial correlation and obvious clustering regions. spatio-temporal scanning analysis showed that most COVID-19 high incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR = 491.57), which was 491.57 times the aggregation risk of other regions. Among the meteorological variables, the daily average temperature, wind speed, precipitation, and new COVID-19 cases were negatively correlated. The air pollution concentration and migration index were positively correlated with new confirmed cases, and the government response strict index was strongly negatively correlated with confirmed COVID-19 cases. Conclusions Environmental temperature has a certain inhibitory effect on the transmission of COVID-19, and the air pollution concentration and migration index have a certain promoting effect on the transmission of COVID-19. The strict government response index indicates that the greater the intensity of government intervention, the fewer COVID-19 cases will occur.

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“…Eventually, there would be more person-to-person contact, leading to human-to-human transmission. 19 Several studies have suggested the common role of family clusters in developing the ongoing epidemic. A recent study in the UK estimated that contact within households was responsible for roughly 70% of SARS-CoV-2 transmission when widespread community control measures were in place.…”

Section: Discussionmentioning

confidence: 99%

Epidemiological Patterns and Spatial Distribution of COVID-19 Cases in DKI Jakarta (March–December 2020)

Das

Sudaryo

2021

Kesmas: National Public Health Journal

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Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is the causative agent of COVID-19 that began in Wuhan, Hubei Province, China. In Indonesia, the first two cases were reported on March 2, 2020; the first major response to block transmission of the virus was the declaration of large-scale social restrictions (LSSR) or Pembatasan Sosial Berskala Besar (PSBB). This study aimed to identify the epidemiology patterns and spatial distribution of the COVID-19 pandemic in five municipalities of DKI Jakarta. The research design comprised an ecological and case-series study uncovering the epidemiological trends and distribution of COVID-19 in DKI Jakarta based on secondary surveillance data. The results from the data analyzed between March-December 2020 showed an increasing epidemiological trend due to COVID-19, and Central Jakarta was the municipality most affected due to pandemic during this period. The implementation of the first PSBB in DKI Jakarta reduced the average number of daily cases during the first month, although the decrease was not statistically significant. There was a spatial autocorrelation of COVID-19 with the neighboring urban villages. There were fifteen COVID-19 hotspots all over DKI Jakarta based on the data analyzed in December 2020.

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The Risk Distribution of COVID-19 in Indonesia: A Spatial Analysis

Cited by 23 publications

References 6 publications

The Distribution Pattern of Covid-19 in Indonesia: Study Using Spatial Autocorelation

The Distribution Pattern of Covid-19 in Indonesia: Study Using Spatial Autocorelation

Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

Epidemiological Patterns and Spatial Distribution of COVID-19 Cases in DKI Jakarta (March–December 2020)

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