AHP and GIS-based Risk Zonation of COVID-19 in North East India

Mahato, Ranjit; Bushi, Dhoni; Nimasow, Gibji

doi:10.12944/cwe.15.3.29

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Some researchers have applied AHP for COVID-19-related research. For example, Mahato et al (2020) analyzed COVID-19 zoning in India using AHP and a geographical information system. This study presents a thematic map categorizing the country's states into six risk zones.…”

Section: Related Workmentioning

confidence: 99%

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County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

Hettiarachchi,

Sun,

et al. 2024

JSIT

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Purpose The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively. Design/methodology/approach In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment. Findings The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge. Originality/value To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

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Section: Related Workmentioning

confidence: 99%

“…Some researchers have applied AHP for COVID-19-related research. For example, Mahato et al. (2020) analyzed COVID-19 zoning in India using AHP and a geographical information system.…”

Section: Related Workmentioning

confidence: 99%

County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

Hettiarachchi,

Sun,

et al. 2024

JSIT

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“…Also, it is necessary to rely on the opinion of experts or the existing domain knowledge to distinguish the criteria (Malczewski, 2000). In addition, the analytic hierarchy process (AHP) has been used to assist in systematic weighting decisions in a spatial model of the COVID-19 pandemic (Mahato et al, 2020, Rahman et al, 2021and Shadeed and Alawna, 2021. However, the results may vary because of the complex decision making involved and they cannot be assumed to be completely correct (Malczewski, 2006 andFeizizadeh et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Integration with GIS for the Analysis of Areas Vulnerable to the Outbreak of COVID-19 in Bangkok, Thailand

2022

IJG

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The COVID-19 pandemic prompted a search for a new method of preventing the spread of this virus. This study established a model of the areas in Bangkok which were vulnerable to the COVID-19 pandemic by using a combination of the Bayesian network (BN) and the geographic information system (GIS). The model was developed using a data-driven approach and was evaluated with 10-fold cross validation and ROC analysis. The results demonstrated that the proposed method effectively predicted the vulnerability of disease outbreak. The most vulnerable areas to the pandemic were around the center and in the west of Bangkok, while the areas of low vulnerability were found in the north and east of the city. Population density and the aerosol index were highly influential factors in the outbreaks, affirmed by sensitivity analysis. Furthermore, the model used to conduct a scenario analysis resulted in the identification of vulnerability management strategies.

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“…In addition to pathological factors, the vulnerability of social factors, such as population density, age structure of the population, ethnicity, and regional health levels, is also worth studying (Ong et al 2021 ; DeCaprio et al 2020 ; Chen et al 2021 ). In these studies, the AHP method was also applied to the assessment of regional risk levels (Rahman et al 2020 ; Mahato et al 2020 ; Sarwar and Imran 2021 ).…”

Section: Introductionmentioning

confidence: 99%

An AHP-based regional COVID-19 vulnerability model and its application in China

Gao

Jiang

et al. 2021

Model. Earth Syst. Environ.

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Since the COVID-19 outbreak, four cities—Wuhan, Beijing, Urumqi and Dalian—have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed. Supplementary Information The online version contains supplementary material available at 10.1007/s40808-021-01244-y.

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AHP and GIS-based Risk Zonation of COVID-19 in North East India

Cited by 6 publications

References 4 publications

County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

Bayesian Network Integration with GIS for the Analysis of Areas Vulnerable to the Outbreak of COVID-19 in Bangkok, Thailand

An AHP-based regional COVID-19 vulnerability model and its application in China

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