PMID- 34604502
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220824
IS  - 2363-6203 (Print)
IS  - 2363-6211 (Electronic)
VI  - 8
IP  - 3
DP  - 2022
TI  - Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM 
      approach: a case study of West Bengal, India.
PG  - 3103-3116
LID - 10.1007/s40808-021-01287-1 [doi]
AB  - COVID-19 is a worldwide transmitted pandemic that has brought a threatening 
      challenge to Indian society and the economy. The disease has become a public 
      health disaster, which has no effective medication. However, proper management 
      and planning, which includes understanding the transmitting pattern, number of 
      containment zones, vulnerable factors, and level of risk, may break the chain of 
      transmission and reduce the number of cases. Hence, this study has attempted to 
      model the COVID-19 vulnerability using an integrated fuzzy multi-criteria 
      decision-making (MCDM) approach, namely fuzzy-analytical hierarchy process (AHP) 
      and fuzzy-technique for order preference by similarity to ideal solution 
      (TOPSIS) for West Bengal, India, through geographic information system (GIS). A 
      total of 15 parameters were utilised to model the COVID-19 vulnerability, which 
      was further categorised into three criteria: social vulnerability, 
      epidemiological vulnerability, and physical vulnerability. The final 
      vulnerability mapping has been done using these three criteria through the GIS 
      platform. This study reveals that COVID-19 infection highly threatens about 20% 
      of the total area of West Bengal, 23.42% moderately vulnerable, and 57.03% of the 
      area comes under low vulnerability. The highly vulnerable region includes the 
      Kolkata, South 24 Paraganas, and North 24 Paraganas, which are considered highly 
      populated districts of West Bengal. Therefore government agencies should be more 
      focused and plan accordingly to safeguard the community, especially the region 
      with very high COVID-19 vulnerability, from further spreading the infection.
CI  - (c) The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.
FAU - Malakar, Sukanta
AU  - Malakar S
AUID- ORCID: 0000-0003-3366-2388
AD  - Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of 
      Technology Kharagpur, Kharagpur, West Bengal 721302 India. GRID: grid.429017.9. 
      ISNI: 0000 0001 0153 2859
LA  - eng
PT  - Journal Article
DEP - 20210927
PL  - Netherlands
TA  - Model Earth Syst Environ
JT  - Modeling earth systems and environment
JID - 101675633
PMC - PMC8475317
OTO - NOTNLM
OT  - COVID-19 vulnerability
OT  - Fuzzy-AHP
OT  - Fuzzy-TOPSIS
OT  - GIS
OT  - MCDM
OT  - West Bengal
COIS- Conflict of interestThere is no conflict of interest to declare.
EDAT- 2021/10/05 06:00
MHDA- 2021/10/05 06:01
PMCR- 2021/09/27
CRDT- 2021/10/04 06:11
PHST- 2021/06/05 00:00 [received]
PHST- 2021/09/15 00:00 [accepted]
PHST- 2021/10/05 06:00 [pubmed]
PHST- 2021/10/05 06:01 [medline]
PHST- 2021/10/04 06:11 [entrez]
PHST- 2021/09/27 00:00 [pmc-release]
AID - 1287 [pii]
AID - 10.1007/s40808-021-01287-1 [doi]
PST - ppublish
SO  - Model Earth Syst Environ. 2022;8(3):3103-3116. doi: 10.1007/s40808-021-01287-1. 
      Epub 2021 Sep 27.