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Journal of Intelligent & Fuzzy Systems 43 (2022) 4555–4564
DOI:10.3233/JIFS-213279
IOS Press
4555
Fuzzy VIKOR approach to identify
COVID-19 vulnerability region to control
third wave in Assam, India
Bhimraj Basumatarya, Nijwm Warya, Jeevan Krishna Khaklaryband Harish Gargc,∗
aDepartment of Mathematical Sciences, Bodoland University, Kokrajhar, Assam, India
bDeparment of Mathematics, Central Institute of Technology, Kokrajhar, Assam, India
cSchool of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala,
Punjab, India
Abstract. These days, the appraisal of the COVID-19 vulnerability has become a difficult errand for the whole world. The
COVID-19 administration dynamic issue frequently includes numerous elective arrangements clashing standards. In this
paper, we present a multi-criteria decision-making (MCDM) procedure based on the fuzzy VIKOR method to survey the
COVID-19 vulnerability in the state of Assam, India. The trapezoidal fuzzy number is utilized to evaluate the rating of
the loads for the set-up models. We have observed environment, social, and Medical factors after observing the spread of
COVID-19. To study and to have comments, a committee of five experts has been formed from a different region of Assam to
observe and comment to identify Coronavirus’s weakest factors. For a better survey, we have divided the state into four areas
namely Rural Area, Urban Area, Market Area in Rural Area, and Market Area in Urban Area. The current research looked
at how the fuzzy VIKOR selects provinces for urgent adaptation needs differently than a traditional MCDM technique.
Keywords: Assam, COVID-19, trapezoidal fuzzy number, fuzzy VIKOR, vulnerability region
1. Introduction
The novel Coronavirus, designated 2019-nCoV,
infected a few people in Wuhan, China in Decem-
ber 2019, and since then, the outbreak has spread to
over 200 countries a worldwide. This has driven the
World Health Organization (WHO) to proclaim it as
worldwide general wellbeing crisis. Legislatures of
the countries influenced by this pandemic are going
around defining arrangements and giving assets to
deal with this scourge. Gauging the disease rate for
a country can go about as a colossal resource in
arranging and detailing approaches for such coun-
tries. While no model can precisely figure out the
∗Corresponding author. Harish Garg, School of Mathematics,
Thapar Institute of Engineering & Technology, Deemed Uni-
versity, Patiala 147004, Punjab, India. E-mail: harishg58iitr@
gmail.com.
pace of contamination and mortality, endeavours have
been made to consider and examine the qualities and
weaknesses of numerous examinations and models
introduced with respect to the COVID-19. Though the
gauge models utilized by the wellbeing office or the
Government of India were not unveiled, we can pro-
ceed with existing models in isolated distributions.
Every one of these models adopted various strategies
and procedures to anticipate future rates.
There has been a bounty of accessible numeri-
cal procedures to foresee the disease rate for the as
of now continuous COVID-19 emergency. In past
exploration [1], analysts assessed the presence of a
larger part of these procedures and closed with two
models which can be utilized for additional reasons
for assessing the number of cases influenced by the
COVID as these models gave the best forecasts.
These two models, exponential bend fitting and least
ISSN 1064-1246/$35.00 © 2022 – IOS Press. All rights reserved.
4556 B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19
square fitted model can be utilized for the present
moment and long haul estimating individually. The
tale of the Coronavirus rose in Wuhan wet market,
China in December 2019, and the virus had gradu-
ally spread across China and to numerous different
nations via people traveling to and from China. Since
the rise of this infection in December 2019, the num-
ber of tainted cases from China brought into different
nations is on the ascent, and the epidemiologic picture
is changing consistently [2]. In [35], authors studied a
predictive analytics model for COVID-19 pandemic
using artificial neural networks. Also, in [36], authors
studied the impact of COVID-19 pandemic on the
Turkish civil aviation industry.
We assessed the vulnerability of COVID-19 in the
Assam provinces with the fuzzy VIKOR (FV). The
current research looked at how the fuzzy VIKOR
selects provinces for urgent adaptation needs differ-
ently than a traditional MCDM technique. We used
the fuzzy VIKOR because it is a compromise option
that takes into account both group utility and oppo-
nent regret. Compensation between these two criteria
is especially important for province vulnerability
assessments, as vulnerability rankings are frequently
translated into rankings for prioritizing adaptation
needs. Given the considerable effects of COVID-19,
the adaption prioritizing across provinces should also
evaluate overall pleasure and remorse over choosing
the wrong provinces (alternatives).
1.1. Purpose of the study
The primary purpose of the study is to identify
the COVID-19 vulnerability region to control third
or further waves in Assam, India. Also, the research
aims to throw light on the awareness of COVID-19
like symptoms, environment effect, social distance,
etc. criteria in Assam. Considering all of these points,
the authors reviewed some important points with the
real case of Assam, India. For better the study and to
control the further wave of COVID-19 in Assam, the
following research questions have been raised:
1) Possible spread of COVID-19 in Rainy Day,
Cold Day, and Sunny Day.
2) Maintaining Social Distance in Assam.
3) Lately, Quarantine lockdown in Assam.
4) Lately, declaration of emergency.
5) Lately, restriction on internal border restric-
tion reduced the ability to move freely.
6) Lack of restrictions on nonessential govern-
ment service.
7) Lack of restrictions on mass gathering.
8) Not follow the curfew.
9) Not maintaining Health Monitoring.
10) Lack of health testing.
11) Lack of quarantine of patients and those sus-
pected of infection.
12) Government policies that affect the coun-
try’s resources (Especially materials Health-
Workers).
13) Due to the lack of fewer Medical workers
(Medical staff).
1.2. Motivation
We assessed the vulnerability of COVID-19 in the
Assam provinces with the fuzzy VIKOR. The present
study focused on how the fuzzy VIKOR makes
different selections of provinces for urgent adapta-
tion needs compared with a conventional MCDM
approach. We employed the fuzzy VIKOR because
the VIKOR provides a compromise solution, con-
sidering both group utility and the regret of an
opponent. Such compensation between these two
factors is particularly critical for the vulnerability
assessment of provinces, as the vulnerability rankings
are often translated to the rankings for prioritiz-
ing the provinces’ adaptation needs. Additionally,
the adaptation prioritization among provinces should
consider the overall satisfaction and regret of the
selection of wrong provinces (alternatives), given the
significant effects of COVID-19. This study con-
tributes by using fuzzy mathematics and VIKOR
multi-criteria decision making (MCDM) technique
to demonstrate how different criteria’s/information
related to COVID-19 providers could be ranked on
several established criteria. Fuzzy VIKOR appears
as powerful tool in allowing multiple expert opinions
in the same model. The major contributions of the
study are
1) The COVID-19 Vulnerability Region in Assam
is identified to control the third or further wave
of COVID-19 with the proposed model.
2) Different criteria (or possible ways) for
the spread of COVID-19 are identified and
arranged ranking-wise.
3) In the end, the advantages, comparative anal-
ysis, and limitations of the proposed study are
discussed, to prove the effectiveness and nov-
elty of the study.
B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19 4557
2. Materials and methods
2.1. About VIKOR strategy
VIKOR strategy was created for multi-standards
optimization of complex frameworks. It decides the
trade-off positioning list, the trade-off arrangement,
and the weight dependability span for inclination
soundness of the trade-off arrangement acquired with
the underlying (given) loads. VIKOR centres on posi-
tioning and choosing from a lot of options within
the sight of clashing measures. Opricovic et al. [9]
considered two MCDM strategies, VIKOR Method
(VM) and TOPSIS Method (TM) which are looked
at, zeroing in on demonstrating the accumulating
capacity and normalization, to uncover and to anal-
yse the procedural premise of these two MCDM
techniques. VM strategy presents the positioning list
dependent on the specific proportion of “closeness” to
the ideal arrangement by utilizing direct standardiza-
tion. Opricovic [10] studied civil engineering systems
by multi-criteria optimization method. Liou et al.
[8] used VM to analyses the management level of
Taiwan’s domestic carriers and to identify the gaps
between what aircraft deliver and what consumers
seek, while Sanayei et al. [13] used VM to position
providers in a flexible chain framework. Later, many
authors used the application of fuzzy VICKOR and
fuzzy MCDM [11, 12, 14–30, 37–42] in different
fields of science and technology. Garg et al. [31] stud-
ied VIKOR methods for complex q-rung orthopair
fuzzy sets and their applications. In [32], authors
have presented an algorithm for T-spherical fuzzy
multi-attribute decision making based on improved
interactive aggregation operators.
2.2. Data set preparation
We’ll start by discussing some relevant issues in
Assam so that readers can get a sense of the state’s
demographics. Assam is a North-Eastern Indian state.
Assam had a population of 31.2 million people in
2011, according to Indian Census data. According
to data from Unique Identification India, Assam’s
predicted population is 35.6 million as of May 31,
2020. The state’s entire area is 78,438 square kilo-
meters, with a population density of 397 people per
square kilometer. Worldometers.info [3] can effi-
ciently provide daily information on India’s total
complete number for COVID-19. This source, on
the other hand, shows the relevant information about
Assam starting on August 16th, rather than from the
beginning. We had to resort to the accompanying
in order to obtain Assam-related information from
the very beginning. From 31st March 2020, the day
on which the first COVID-19 case was detected in
Assam, through 19th August 2020, daily information
is available on the COVID-19 Pandemic in Assam
portal [4].
This portal’s data was not updated after August 19.
So, we needed to depend on the data made acces-
sible online by the Assam COVID-19 Dashboard,
Govt. of Assam [5] to get the data we required. In
any case, information for each current day is only
accessible in this article. To obtain prior informa-
tion, it was necessary to regularly monitor changes,
which we did, and we now have the relevant informa-
tion up to the present day. Day-by-day information is
available on Worldometers.info [6] beginning August
16 in any case, as previously mentioned. It is real-
ized that patients with comorbidities should play
it safe to abstain from getting contaminated with
the SARS CoV-2 as they have the most noticeably
terrible anticipation (see for instance [7]). The num-
ber of deaths due to SARS CoV-2 in Assam has
been determined, but it does not include the deaths
of individuals (with comorbidities) who died after
recovering from the COVID-19 infection. Undoubt-
edly this may really have been the followed standards
somewhere else likewise the world over. As a result,
estimates based on typical epidemiological models
will be invalid.
In this article, we will introduce data obtained
consistently from the passage Assam COVID-19
Dashboard, Govt. of Assam, from March onwards.
This information corresponds to the Worldome-
ters.info data from March onwards. In light of the
way that the Assam Government Portal is refreshed
each day, we are entranced to give information from
March to October.
2.3. Sampling and collecting data
The data collecting process was conducted online
through WhatsApp, Gmail, Facebook platforms.
Because this is a new study in the context of Assam,
the questionnaire was surveyed in two phases: The
first phase, the research survey on five experts to
assess the understandable and logical level of the
questionnaire. After collecting opinions, appropriate
contextual adjustments were made and then con-
ducted in the second phase. Phase 2, the data for
affected by COVID-19 was officially collected from
31st March 2020 to 15th March 2021. The online
4558 B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19
Table 1
COVID-19 cases from March 2020 to April 2021 in Assam
Dates COVID-19 cases Dates COVID-19 cases
31st March 2020 01 (First Case) 01st September 2020 111724
01st April 2020 13 05th September 2020 123922
30th April 2020 42 10th September 2020 135805
05th May 2020 44 16th September 2020 148969
07th May 2020 53 19th September 2020 155453
14th May 2020 86 21st September 2020 159320
25th May 2020 548 02nd October 2020 183812
27th May 2020 783 06th October 2020 188902
31st May 2020 1361 19th October 2020 201404
01st June 2020 1485 27th October 2020 204171
05th June 2020 2243 1st November 2020 206514
10th June 2020 3285 8th November 2020 208786
18th June 2020 4904 23rd November 2020 211679
25th June 2020 6646 3rd December 2020 213168
30th June 2020 8407 13th December 2020 214654
1st July 2020 8955 23rd December 2020 215677
5th July 2020 11001 31st December 2020 216208
10th July 2020 15536 3rd January 2021 216304
15th July 2020 19754 1st February 2021 217154
25th July 2020 31086 10th February 2021 217267
31st July 2020 40269 16th February 2021 217309
01st August 2020 41726 6th March 2021 217649
05th August 2020 50445 15th March 2021 217797
10th August 2020 61737 29th March 2021 218310
15th August 2020 75558 1st April 2021 218470
19th August 2020 84317 20th April 2021 226326
25th August 2020 94592 27th April 2021 240676
31st August 2020 109040
survey was conducted over two months, from July
to August 2021. The questionnaire was constructed
by observing different criteria as discussed in the
introduction section.
From the data (Table 1) it is seen that on 12th
August 2020, the state also reported 4,593 COVID-
19 positive cases, the highest single-day spike. From
Table 1, it may be observed that there has been
a steady increase in the number of cases over the
months till the spurt of the Second Wave of the virus
whose effect can be seen in the jump in the number of
cases in the week between 20th April and 27th April
where there is a significant increase of about 14,350
cases in a week.
3. Proposed work
In this section, we are going to use the extended
version of the Fuzzy VIKOR (FV) Method. It is
focused on finding the best alternatives and com-
promise solutions to conflict criterion problems are
determined. The steps in the FV process are as fol-
lows:
Step 1: Create the weight vector and fuzzy decision
matrix.
Step 2: Orchestrating the dynamic gathering and
describing a lot of pertinent ascribes. Idea plan
determination requires recognizable proof of choice
models, and afterward assessment scales are set up to
rank the ideas.
Step 3: Aggregate the decisions makers’ (experts’)
opinions to construct a fuzzy decision matrix and get
aggregated fuzzy weights of criteria.
Step 4: Assume that the nth expert’s fuzzy rating
and weight are χpqr =(χpqr1,χpqr2,χpqr3,χpqr4),
and ωqr =(ωqr1,ωqr2,ωqr3,ωqr4). As a result, the
aggregated fuzzy rating χpq of alternatives for each
criterion can be determined as
χpq =(χpq1,χ
pq2,χ
pq3,χ
pq4),
where χpq1=min(χpqr1), χpq2=1/r χpqr2,χpq3=
1/r χpqr3,χpq4=max(χpqr4).
The total fuzzy weight ωqof each criterion can be
determined as follows:
ωq=(ωq1,ωq2,ωq3,ωq4), where ωq1=min(ωqr1),
ωq2=1/r ωqr2,ωq3=1/r ωqr3,ωq4=max
(ωqr4).
Step 5: Each criterion’s fuzzy weights are trans-
formed to crisp values, and the fuzzy decision matrix
is defuzzified.
B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19 4559
Fig. 1. Flowchart of the proposed study.
Step 6: All criterion’s best Gq∗and worst
Gq−values were determined as Gq∗=max(χpq) and
Gq−=min(χpq).
Step 7: Calculated the values of Spand Rpby the
following relations ([9])
Sp=ωqGq
∗−Gpq
Gq
∗−Gq
−,Rp=max ωqGq
∗−Gpq
Gq
∗−Gq
−
Now we have values of Qp=Vq(Sp−S∗)
S−−S∗+
Vq(Rp−R∗)
R−−R∗, where S∗=min Sp,S−=max Sp,
R∗=min Rp,R−=max Rpand 1 −Vis rep-
resents weight of individual regret, and Vis the
maximum group utility approach. S,Rand Qmust
all be calculated.
Step 8: To rank the alternatives, sort them S,Rand
Qvalues in ascending order.
Step 9: Propose a compromise solution based on
the alternative Ap, which is the best-ranked solution
according to the measure Q(minimum).
The proposed framework of the research can be
shown in the flowchart given in Fig. 1.
Table 2
Linguistic variable table for each criterion
Linguistic Variable Fuzzy Number
Very High () (.8,.9, 1, 1)
High (χ) (.7,.8,.8,.9)
Medium High (ω) (.5,.6,.7,.8)
Medium (ψ) (.4,.5,.5,.6)
Medium Low (ε) (.2,.3,.4,.5)
Low (μ) (.1,.2,.2,.3)
Very Low (τ) (0, 0,.1,.2)
4. Result and discussion
In Table 1, we looked into the COVID-19 outbreak
in Assam, India. We observed that the spread pattern
is exponential, with no signs of a reduction in the near
future. As a result, predicting the peak of the pan-
demic in Assam is still impossible. The COVID-19
condition in this Indian state between March 2020 and
April 2021 might be described as quite concerning.
Even if the situation in India as a whole change, it’s
possible that the spread in Assam will continue to
increase rapidly.
The steps of rating of COVID-19 vulnerability
region can be defined as follows:
Step 1: By observing the spread of COVID-19,
we have observed three alternatives namely Envi-
ronment factors, social factors, and Medical factors.
So as to locate the most weakness (vulnerability)
options, a specialist board of trustees of five experts,
E1,E2,E3,E4and E5has been shaped. These special-
ists are from various departments, two are Doctors,
two are professors and one is a research scholar.
In light of the writing (survey), in regards to the
assessment of Coronavirus weakness on the mod-
els and sub-measures things were examined with the
specialists. By observing the most common hydro-
logic vulnerability in the COVID-19 approach, the
COVID-19 basin of the state is divided into four
sub-basin regions as follows:
A1- Rural Area
A2- Urban Area
A3- Market Area in Rural Area
A4- Market Area in Urban Area
Step 2: Orchestrating the dynamic gathering and
describing a lot of pertinent ascribes. Idea plan
determination requires recognizable proof of choice
models, and afterward assessment scales are set up so
as to rank the ideas. These rules must be characterized
by the corporate techniques.
4560 B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19
Table 3
Weight of criteria provided by experts
Experts/criteria C1C2C3C4C5C6C7C8C9C10 C11 C12 C13 C14 C15
E1χχχχχχχ χ χ ω ω ω
E2χτχτττττ ω ψ τ ψ
E3ψχψψμψψχ ψ μ μ ψ ψ μ
E4τττττψ ψ ψ
E5μμωχχχχ χ χ χ ψ
Table 4
Appraisals of sub-basins provided by experts
Decision makers Alternatives C1C2C3C4C5C6C7C8C9C10 C11 C12 C13 C14 C15
E1A1χχχχχχχ χ χ ω ω ω
A2ψψ ε χ χ μ χ χ χ χ χ χ χ
A3χχχχχ ω ω ω χ
A4ψψε χμχχχμ χ χ χ
E2A1χτχτττττ ω ψ τ ψ
A2ωτωτμμτ τ ω ψ τ ψ
A3χτχττττμω ψ τ ψ
A4χτχτμμτ ψ ω ψ τ ψ
E3A1ψχψψμψψχ ψ μ μ ψ ψ μ
A2ψχψψτ ωψ ω ψ ψ ψ ψ μ
A3ψχψψμψψχ ω μ μ ψ ψ μ
A4ψχψψτ ωψ ω ψ ψ ψ ψ μ
E4A1τττττψ ψ ψ
A2ττττμωχ χ ψ
A3τττττψ ψ ψ
A4ττττμωχ χ ψ
E5A1μμωχχχχ χ χ χ ψ
A2μμωχχωχωχχχ
A3μμωχχχχ ω χ χ ψ
A4μμωχχωχχ χχχ
A committee of five experts E1,E2,E3,E4and
E5has been formed to select the most assessment
of Coronavirus vulnerability. The following criteria
have been defined
•Rainy Day (C1)
•Cold Day <25◦C(C2)
•Sunny Day >30◦C(C3)
•Not maintaining Social Distance (C4)
•Lately Quarantine lockdown (C5)
•Lately declaration of emergency (C6)
•Lately restriction on internal border restriction
reducing the ability to move freely (C7)
•Lack of restrictions of nonessential government
service (C8)
•Lack of restrictions of mass gathering (C9)
•Not follow the curfew (C10)
•Not maintaining Health Monitoring (C11)
•Lack of health testing (C12)
•Lack of quarantine of patients and those sus-
pected of infection (C13)
•Government policies that affect the country’s
resources (especially materials Health Workers)
(C14)
•Due to lack of fewer Medical workers (Medical
staff) (C15).
Step 3: We define the appropriate etymological
factors for model significance weights and fuzzy rat-
ings for choices concerning each measure, and then
these semantic factors can be presented as trapezoidal
fuzzy numbers. Five experts utilized the phonetic
weighting factors to survey the significance of the
models. Experts have controlled the significant loads
of the measurements, which are shown in Table 3.
Table 4 shows the experts’ evaluations of the four idea
plans (for four sub basins) using various metrics.
Step 4: Assume that the nth expert’s fuzzy rating
and weight are χpqr =(χpqr1,χpqr2,χpqr3,χpqr4),
and ωqr =(ωqr1,ωqr2,ωqr3,ωqr4). As a result, the
aggregated fuzzy rating χpq of alternatives for each
criterion can be determined as in Table 6.
Step 5: Each criterion’s fuzzy weights are trans-
formed to crisp values, and the fuzzy decision matrix
is defuzzified, shown as Table 6.
Step 6: All criterions’ best Gq∗and worst Gq−
values were given in Table 7.
B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19 4561
Table 5
Aggregated fuzzy weights
Weight A1A2A3A4
C1(0,.46,.54,.90) (0,.46,.48,.90) (0,.36,.40,.80) (0,.46,.48,.90) (0,.40,.42,.90)
C2(0,.36,.40,.90) (0,.36,.40,.90) (0,.30,.34,.90) (0,.36,.40,.90) (0,.30,.34,.90)
C3(0,.54,.58,.90) (0,.54,.58,.90) (0,.40,.48,.80) (0,.54,.58,.90) (0,.44,.50,.90)
C4(.80,.90, 1, 1) (.80,.90, 1, 1) (.70,.88,.96, 1) (.80,.90, 1, 1) (.80,.90, 1, 1)
C5(0,.42,.46,.90) (0,.42,.82,.90) (0,.42,.46,.90) (0,.42,.46,.90) (0,.42,.46,.90)
C6(0,.38,.44, 1) (0,.38,.44, 1) (0,.28,.30, 1) (0,.38,.44, 1) (0,.28,.30, 1)
C7(0,.52,.54,.90) (0,.52,.54,.90) (0,.56,.62,.90) (0,.56,.62,.90) (0,.56,.62,.90)
C8(0,.60,.64, 1) (0,.60,.64, 1) (0,.60,.64, 1) (0,.62,.68, 1) (0,.60,.64, 1)
C9(.70,.86,.92, 1) (.70,.86,.92, 1) (.70,.88,.96, 1) (.70,.86,.92, 1) (.70,.88,.96, 1)
C10 (0,.60,.64, 1) (0,.60,.64, 1) (0,.56,.62, 1) (0,.58,.66, 1) (0,.58,.60, 1)
C11 (.10,.68,.74, 1) (.10,.68,.74, 1) (.40,.74,.80, 1) (.10,.70,.78, 1) (.40,.76,.84, 1)
C12 (.10,.62,.68, 1) (.10,.62,.68, 1) (.40,.72,.76, 1) (.10,.62,.68, 1) (.40,.72,.76, 1)
C13 (0,.54,.58, 1) (0,.54,.58, 1) (0,.60,.64, 1) (0,.54,.58, 1) (0,.62,.68, 1)
C14 (.40,.66,.70, 1) (.40,.66,.70, 1) (.40,.70,.72, 1) (.40,.66,.70, 1) (.40,.70,.72, 1)
C15 (.10,.54,.58, 1) (.10,.54,.58, 1) (.10,.64,.66, 1) (.10,.58,.60, 1) (.10,.64,.66, 1)
Table 6
Weight of each criterion
C1C2C3C4C5C6C7C8C9C10 C11 C12 C13 C14 C15
Weight .48 .41 .51 .93 .45 .46 .49 .56 .87 .56 .63 .60 .53 .69 .56
A1.46 .42 .51 .93 .54 .46 .49 .56 .87 .56 .63 .60 .53 .69 .56
A2.39 .39 .42 .89 .45 .40 .52 .56 .89 .55 .67 .72 .56 .71 .60
A3.46 .42 .51 .93 .45 .46 .52 .58 .87 .56 .65 .60 .53 .69 .57
A4.43 .39 .46 .93 .45 .40 .52 .56 .89 .55 .75 .72 .58 .71 .60
Table 7
Best and worst values
C1C2C3C4C5C6C7C8C9C10 C11 C12 C13 C14 C15
Gq∗.46 .415 .505 .925 .535 .455 .52 .575 .885 .56 .75 .72 .575 .705 .60
Gq−.39 .385 .42 .885 .445 .395 .49 .56 .87 .545 .63 .60 .53 .69 .555
Table 8
S,Rand Qvalues for all Alternatives
A1A2A3A4
S4.92 4.94 4.05 2.9
R.87 .92 .9 .56
Q.92 1 .7 0
Table 9
Alternatives are ranked in ascending order by S,Rand Q
Rank 1 2 3 4
SA
4A3A1A2
RA
4A1A3A2
QA
4A3A1A2
Step 7: The values of S,Rand Qare shown in
Table 8.
Step 8: Table 9 shows the ranking of the Alterna-
tives.
Step 9: Table 10 shows the ranking of the alterna-
tives.
Table 10
Criteria wise ranking of four alternatives
Ordering of Alternatives from high to low
1. C4A3A4A1A2
2. C9A4A2A3A1
3. C14 A4A2A3A1
4. C11 A4A2A3A1
5. C12 A2A4A1A3
6. C10 A3A1A4A2
7. C8A3A4A2A1
8. C15 A3A4A3A1
9. C13 A4A2A3A1
10. C3A3A1A4A2
11. C7A4A3A2A1
12. C1A3A1A4A2
13. C6A3A1A4A2
14. C5A1A3A4A2
15. C2A3A1A4A2
5. Sensitivity analysis
In this study, sensitivity analysis evaluates the
ranking of alternatives associated with various
criteria. The main study of the paper is to find out the
4562 B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19
Table 11
Ranking of criteria weights
1. C4
2. C9
3. C14
4. C11
5. C12
6. C10
7. C8
8. C15
9. C13
10. C3
11. C7
12. C1
13. C6
14. C5
15. C2
impact of different criteria in their respective rankings
in different sectors like a rural area, urban area, a mar-
ket area in a rural area, and market area in an urban
area of Assam. Table 10 shows criteria wise ranking
of vulnerability of four alternatives discussed in this
study. The result shows that the alternative A1(Rural
Area) is the highest vulnerability in the criteria C5,
alternative A2(Urban Area) is the highest vulnerabil-
ity in the criteria C12, alternative A3(Market Area in
Rural Area) is the highest vulnerability in the criteria
C1,C2,C3,C4,C6,C8,C10 alternative A4(Market
Area in Urban Area) is the highest vulnerability in the
criteria C7,C9,C11,C13 ,C14 respectively and alter-
native A1(Rural Area) is the lowest vulnerability in
the criteria C7,C8,C9,C11,C13 ,C14 and C15, alter-
native A2(Urban Area) is the lowest vulnerability
in the criteria C1,C2,C3,C4,C5,C6,C10,C12 and
alternative A3(Market Area in Rural Area) is the low-
est vulnerability in the criteria C12. Table 11 shows
ranking criteria weights, criteria, C4(Not Maintain-
ing the Social Distance) is the highest weights as per
the experts followed by C9(Lack of Restriction of
Mass Gathering) and C14 (Government policies that
effects the countries resource specially Staff of Health
workers (Medical Stuff)) respectively while criteria
C2(cold day less than 25 C) has the least criteria
weights.
5.1. Comparison
In [33], the authors used fuzzy logic approach to
study prevention from COVID-19 in India. Accord-
ing to the study, the virus remains in the human body
for 14 days. Also, they suggested if somebody trav-
els history from the infected area then she/he has to
undergo quarantine for 14 days. Also, in the study
mainly observation of symptoms of COVID-19 is
focused.
In our study, many different angles are observed
including travel history and symptoms of the patients
by applying the Fuzzy VIKOR method In Assam.
Also, the present study tells of the ranking of the dif-
ferent criteria. This indicates persons alert or careful
to people about the prevention of COVID-19.
In [34], different treatment options for COVID-
19 using fuzzy PROMETHEE and VIKOR methods
are discussed. According to the study overall, there
is no globally approved specific antiviral drug avail-
able for COVID-19. All drug options come from the
experience of treating SARS, MERS, or other new
influenza viruses. Active symptomatic support is the
key to treatment.
In our study, we have used the Fuzzy VIKOR
method to analyze the importance of not maintaining
Health Monitoring, Lack of health testing, Lack of
quarantine of patients and those suspected of infec-
tion, Government policies that affect the country’s
resources (especially materials Health Workers), Due
to lack of less Medical workers (Medical stuff). Also,
these criteria are arranged in systematic systems.
5.2. Advantages of the studies
Our approach has several advantages over the
existing multivariate regression approach as follows:
1) This study shows the approach of Fuzzy
VIKOR to analyze the approach to Identify
COVID-19 Vulnerability Region in Assam,
India to control the third wave or further wave
of COVID-19.
2) The Fuzzy VIKOR can identify the signifi-
cant factors or different criteria of Vulnerability
Region in Assam intention through relative
weights based on experts’ opinions.
3) The benefits of this research will accrue knowl-
edge about the COVID-19 Vulnerability Region
in Assam. Further, this study will show the rank-
ing wise of each criteria Rainy Day, Cold Day,
Sunny Day, Not maintaining Social Distance,
Lately Quarantine lockdown, Lately declara-
tion of emergency, Lately restriction on internal
border restriction reducing the ability to move
freely, Lack of restrictions of nonessential gov-
ernment service, Lack of restrictions of mass
gathering, Not follow the curfew, Not main-
taining Health Monitoring, Lack of health
testing, Lack of quarantine of patients and
B. Basumatary et al. / Fuzzy VIKOR approach to identify COVID-19 4563
those of suspected of infection, Government
policies that effects the country’s resources
(especially materials Health Workers) and Due
to lack of less Medical workers (Medical stuff).
Understanding these factors would enable the
government to optimize its intervention strate-
gies and accelerate the massive important steps
to overcome against COVID-19 and can take the
good initiative to control third wave or further
wave of COVID-19.
6. Conclusion
In this investigation, we evaluated the COVID-19
weakness in the Assam locale with Fuzzy VIKOR.
We characterized the COVID-19 weakness as a com-
ponent of environmental factors, social factors, and
medical elements, and we profiled the critical point-
ers for weakness with the Expert’s decision. Fuzzy
VIKOR technique is a useful apparatus in multi stan-
dards dynamic bargained arrangement which got,
could be acknowledged by the experts since it gives
the greatest gathering utility (represented by the min-
imum value of S) of the larger part, and at least the
individual lament (represented by the minimum value
of R) of the adversary. In this examination, we pro-
posed an altered Fuzzy VIKOR that was upheld by
the OWA administrator and decided loads of rules. As
per the last score, the option A4(least of Q) that the
Market Area in Urban Area is the weakest vulnerable
area followed by A3that is Market Area in rural Area
options are second-most weakness vulnerable area.
The spurt of cases in the second wave of the virus
may be attributed to the gatherings during the recent
election campaigning in the state. We hope this work
will be able to help in controlling the third and further
wave of COVID-19 in Assam, India.
There are some limitations to the study as well.
First, because the laboratory selection problem’s
decision-makers developed a team decision matrix,
aggregation operations were not presented in real-
life applications. Second, the study yielded positive
results when additional experts were included. We’ve
gathered five specialists on this case. The main lim-
itation of the study is that selection of the criteria
related to COVID-19 is challengeable.
For future research suggestions, we would like to
propose different MCDM based on fuzzy and Neu-
trosophic sense to study the impact of COVID-19
in a different community of Assam by taking differ-
ent criteria of the area. Because it is observed that
some community has less influence than other com-
munity. For example, tribal people of Assam who
live exclusively in rural areas or forest areas are less
affected.
Acknowledgment
The authors would like to express sincere thanks
and gratitude to experts for giving valuable sugges-
tions for betterment of the manuscript. Also, the
author (Harish Garg) is grateful to DST-FIST grant
SR/FST/MS-1/2017/13 for providing technical sup-
port.
Supplementary material
The Appendix part is available in the electronic
version of this article: https://dx.doi.org/10.3233/
JIFS-213279.
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