Kualitas Udara Dan Potensi Transmisi Covid-19  Di Pulau Jawa

Zulkarnain, Rizky; Ramadani, Karuniawati Dewi

doi:10.34123/semnasoffstat.v2020i1.398

Cited by 8 publications

(8 citation statements)

References 3 publications

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“…Spatial statistics have mainly been used to analyze the socioeconomic and demographic risk factors of COVID‐19 (Iyanda et al., 2020 ; Sannigrahi, Pilla, Basu, Basu, & Molter, 2020 ; Sun, Matthews, Yang, & Hu, 2020 ; Urban & Nakada, 2021 ). Air quality (Maiti et al., 2020 ; Zulkarnain & Ramadani, 2020 ), health infrastructure (Mollalo, Vahedi, & Rivera, 2020 ), and mobility (Nian et al., 2020 ) have also been examined and linked to COVID‐19 risk and transmission. We have seen an influx of both spatial and spatiotemporal statistical methods applied to COVID‐19, with many studies utilizing the results to produce risk and social vulnerability maps.…”

Section: Resultsmentioning

confidence: 99%

“…For example, a spatial lag model (SLM) can be used to examine how events at a location influence similar events in surrounding locations (i.e., spatial interaction); and a spatial error model (SEM) can be applied to account for autocorrelation of the residuals (Iyanda et al., 2020 ; Maiti et al., 2020 ; Mollalo, Vahedi, et al, 2020 ; Nian et al., 2020 ; Sannigrahi et al., 2020 ; Sun, Di, Sprigg, Tong, & Casal, 2020 ; Urban & Nakada, 2021 ); see Table 1 . For COVID‐19, spatially combined autoregressive models (SAC) have also been used as a combination of the previous models to simultaneously consider spatial lag and spatial error parameters (Sun, Di, et al., 2020 ; Zulkarnain & Ramadani, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Franch-Pardo

Desjardins

Barea-Navarro

et al. 2021

Transactions in GIS

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COVID‐19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID‐19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID‐19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID‐19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID‐19 transmission and the associated risk factors.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Franch-Pardo

Desjardins

Barea-Navarro

et al. 2021

Transactions in GIS

View full text Add to dashboard Cite

show abstract

“…For instance, SLM can be used to scrutinize how actions at a location influence similar actions in nearby locations (i.e., spatial interaction); and SEM can be useful to account for autocorrelation of the residuals ( [49]; [46]; [2]; [21];[40]; [42]; [41]. From Table 1, it can be observed that spatially combined autoregressive models (SAC) have also been used as a mixture of the previous models to concurrently consider SEM and SLM in the study COVID-19 [58]; [69]. [18], [19], [21] and [23] uses SAC to analyze the characteristics of COVID-19 at various spatial levels of aggregation.…”

Section: Global Regression Modellingmentioning

confidence: 99%

Mapping the Pandemic: A Review of GIS-based Spatial Modeling of COVID-19

Aboalyem,

Ismail

2023

Preprint

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According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.

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“…Travaglio dkk. (2021) serta Zulkarnain & Ramadani (2021) menemukan asosiasi positif antara polusi udara dan transmisi COVID-19. Paparan jangka panjang terhadap polusi udara berhubungan dengan risiko beberapa penyakit pernapasan, dimana individu dengan prakondisi kesehatan tersebut akan lebih rentan terhadap infeksi COVID-19 (Zheng dkk., 2021).…”

Section: Kerentanan Pandemiunclassified

Pandemic Vulnerability Mapping in East Java and Its Connection to Local Economic Resistance

Zulkarnain¹,

Ramadani²

2022

EJAVEC

Self Cite

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The objectives of this study are to measuring and to mapping pandemic vulnerability in East Java as well as to examine the association between pandemic vulnerability and local economic resistance. To achieve the objectives, this study employs several techniques: descriptive analysis, principal component analysis, cluster analysis, and analysis of variance. The data were acquired from various sources, that cover health, social, economic, and environmental conditions. The results showed that there were seven most vulnerable districts in East Java. They were Kota Surabaya, Kabupaten Sidoarjo, Kabupaten Gresik, Kota Kediri, Kota Malang, Kota Mojokerto and Kota Madiun. The core pandemic indicators were population mobility, the number of COVID-19 cases and its mortality, population density, air pollution, the prevalence of diabetes, and the share of secondary and tertiary sectors. Furthermore, this study found that there were negative association between pandemic vulnerability and local economic resistance in East Java.

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Kualitas Udara Dan Potensi Transmisi Covid-19 Di Pulau Jawa

Cited by 8 publications

References 3 publications

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Mapping the Pandemic: A Review of GIS-based Spatial Modeling of COVID-19

Pandemic Vulnerability Mapping in East Java and Its Connection to Local Economic Resistance

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