Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system

1. Introduction

The word ‘Corona’ is one of the most widely used words in the recent time. The spread of novel coronavirus disease which is termed as COVID-19 was started from the city of Wuhan, China since late December of 2019 [1], [2], [3], [4], [5]. Fever, fatigue, cough, problem of breath, loss of taste and smell, etc., are the common symptoms of COVID-19 [6], [7]. The primary symptoms of COVID-19 and common flu are almost the same, which makes this virus more dangerous. When a person comes to know that he is COVID-19 positive, some other people had already been infected by him/her, unknowingly. As on the end of 2020, it was reported that among the active COVID-19 cases, majorities are found in mild symptoms, in India [8], [9], [10], [11], [12]. The time of exposure of symptoms takes around four to five days and may remain up to fourteen days [13], [14]. Corona virus mainly infects the lungs which may causes cardiovascular, gastrointestinal, nervous system, liver, kidney and ocular damages [15], [16], [17].

Most of the countries declared COVID-19 crisis as a national disaster. The outbreak of COVID-19 has already shown its harmful impact on mankind, especially on global economy, health sectors, cultures, education systems, politics, etc. The local and global marketing systems, manufacturing sectors are being hampered, significantly, which resulted a large unemployment of man powers all over the world. The major impact of this pandemic is being observed in health sectors. The inclusion of new COVID-19 hospitals has reduced the scopes of admission of the common people to the hospitals due to other major health issues. So, people are being deprived of their regular treatments from the hospitals due to the lack of doctors, nurses and other health workers. The increasing number of COVID-19 cases reduces the number of beds for common people in several hospitals. Many senior citizens were facing several mental health problems due to their loneliness during the lockdown periods. The art and cultural sectors including film and entertainment industries were temporarily closed due to the pandemic of COVID-19. All the major sport events have been either partially postponed or cancelled indefinitely. The pandemic also established its harmful impact on education system, globally. All the government, and private schools and educational institutes are either partially opened or indefinitely closed or running on virtual mode due to this pandemic which are adversely affecting the learning processes of the learners, worldwide. Lastly, the world political systems were also affected due to rescheduling of elections, death of politicians, lack of campaigning due to social distancing, etc.

Up to 30th November, 2020, around 6,36,09,507 people were found as COVID-19 positive, globally. Among them, around 4,39,83,871 people were recovered from this disease; whereas, around 14,74,186 people died due to this pandemic. USA was facing the worse effect of this pandemic at that time as it covered about 22% of total COVID-19 positive cases in the world. Around 1,66,904 new COVID-19 cases were found in USA; whereas, about 1,39,26,349 people were found as infected, of which around 82,24,314 people were recovered from this disease, and about 2,74,369 people died. According to the statistics of worldwide COVID-19 report on 30th November, 2020, India stood second in terms of number of COVID-19 positive cases. According to the data provided by the Department of Ministry of Health and Family Welfare (MoHFW), Government of India on 30th November, 2020, around 94,31,691 people were infected, of which about 88,47,600 people were recovered, and around 1,37,139 people lost their lives due this pandemic in India. These facts reflected very serious concern to the Government of India in controlling the spread of COVID-19. On 26th March, 2020, Nirmala Sitharaman, the finance minister of India, announced an incentive package of crore in the purpose of helping the people affected by the lockdown. Most of the states and union territories (UTs) of India are facing the worse effect of COVID-19. But, as per the available report of MoHFW as on 30th November, 2020, one UT, viz., Lakshadweep remained free from COVID-19; and consequently, the administration of Lakshadweep requested to the central government to provide the permission of reopening the schools; but, other states and UTs were still hesitating to reopen in that circumstances.

It is the fact that most of the news channels, survey institutes or some other organizations are estimating the fight of a country or state against COVID-19 on the basis of confirmed corona positive cases. But, as per the recent report of World Health Organization (WHO), majority of the active COVID-19 cases are found either asymptomatic or in mild symptoms. Therefore, a country or state having large number of population or performing huge number of COVID-19 tests would obviously situate in the group of large number of positive COVID-19 cases. Thus, some other factors such as population, population density, number of COVID-19 tests, recovery rate, mortality rate, etc., are needed to be considered in assessing the fight of a country or state against COVID-19.

In this article, an effort is made for estimating the strategies which were taken by the states and UTs in India to combat against COVID-19. In this purpose, a methodology is developed to evaluate the performance score to control the spread of COVID-19 corresponding to each state and UT in India. The data were collected from the official website of MoHFW, Government of India. But, such a highly populated country like India, it is very difficult to find exact figures of relevant inputs, such as population of a state or UT, number of tests, confirmed cases, recovery rate, mortality rate, etc. Therefore, some sorts of uncertainties or inexactness are unavoidably occurred in collecting data. Thus, the inclusion of fuzziness in the process of data collection becomes very much essential. The concept of fuzziness was successfully implemented by Togacar et al. [18] for detection of COVID-19. Later, Govindan et al. [19] used fuzzy inference system to develop a decision support system for demand management in healthcare supply chains concerning COVID-19 outbreaks. Mardani et al. [20] proposed an extended approach based on hesitant fuzzy sets to rank the key challenges of digital health interventions in controlling COVID-19 outbreak. Mahmoudi et al. [21] developed a fuzzy clustering method for comparing the infection rate of COVID-19 in countries with high risks. Behnood et al. [22] used adaptive neuro-fuzzy inference system in determining the infection rate of the COVID-19 in U.S. Later, Ly [23] developed a methodology using adaptive neuro-fuzzy inference system for forecasting COVID-19 cases in United Kingdom. A decision making approach was proposed by Singh and Avikal [24] for prioritizing the preventive activities against COVID-19. Further, Ocampo and Yamagishi [25] performed an intuitionistic fuzzy DEMATEL analysis to develop a model of lockdown relaxation protocols concerning the COVID-19 pandemic under the Government of Philippine. The concept of hesitant fuzzy sets was used by Ren et al. [26] in selecting the medicines for the patients having mild symptoms of COVID-19. Li et al. [27] developed a consensus model for managing non-cooperative behaviours of individuals in group decision making problems during the pandemic of COVID-19. Shaban et al. [28] introduced a strategy for detecting COVID-19 patients using fuzzy inference engine and deep neural network. Further, Aggarwal et al. [29] proposed an approach to compare the criterion in decision support system for COVID-19. Ghorui et al. [30] used hesitant fuzzy multi-criteria decision making method to identify the risk factors involved with the spread of COVID-19. To select drugs for the patients with mild symptoms of COVID-19, a decision-making framework using hesitant fuzzy sets was introduced by Mishra et al. [31]. Later, Ecer and Pamucar [32] proposed a technique under intuitionistic fuzzy environment for ranking the performance of insurance companies during COVID-19 pandemic. In the recent past, Sharma et al. [33] developed a meditative fuzzy logic model to provide the relation between the growths of COVID-19 cases with respect to time. But no method has been developed yet to evaluate the status of provinces affected by COVID-19 on the basis of the factors influencing the pandemic situation of COVID-19 using fuzzy systems. Getting informed about the recent status through this method, the concerned governing bodies might think about starting intra-state, and inter-state public transport services, reopening educational institutes, theatres, cinema halls, and museums, supplying medical equipment, declaring economic packages, awarding role model against COVID-19, building up strategies for equitable distribution of COVID-19 vaccines, and others.

In the proposed study, a Mamdani fuzzy inference system (MFIS) [34] is generated to evaluate the state wise performance score to combat against COVID-19 in India. The score value of all the input factors, such as population density, number of COVID-19 tests per million, number of confirmed case per million, recovery rate, mortality rate corresponding to all the states and UTs in India are fuzzified to capture the uncertainties associated with the collected data. Here, linear type fuzzy numbers are used to represent the membership functions (MFs) for input and output parameters of the proposed MFIS. To establish the application potentiality and validity of the proposed methodology, Pearson’s correlation coefficient [35] is evaluated between the final results and recovery rate of COVID-19. Later, a sensitivity analysis is also performed to observe the changes of final output by varying the tolerance ranges of the input values.

2. Data collection and analysis

In the proposed methodology, all the factors such as projected population in 2019, population density, number of COVID-19 tests, confirmed cases of COVID-19, recovery rate, mortality rate, etc., are considered as inputs for assessing the performance of all the states and UTs in India to combat against COVID-19. The projected population in 2019 and population density in 2011 of all the states and UTs were collected based on the census report of India which was conducted by National Commission on Population in 2011 [36]. The data regarding other inputs, such as number of tests, confirmed cases, recovered cases and deceased cases of COVID 19, as shown in Fig. 1, were collected from the official website of MoHFW, Government of India [37] and from another website [38]. But, it is very difficult to assign exact number against each of the above mentioned inputs for such a highly populated country like India due to continuous spread of COVID-19. Also, due to several issues like complexity in accounting exact figures of comorbidity, migratory cases, lack of communication between central and state governments, etc., a deviation up to 2% in the input values available on the website of MoHFW is inevitable. Therefore, the occurrence of uncertainties or inexactness associated with the data collection is unavoidable. Thus, all the crisp inputs for the proposed methodology are fuzzified into suitable fuzzy numbers to capture imprecision associated with the collected data. Since, the uncertainties or inexactness in collected data are found as a deviation of about 2% from the exact value, the triangular fuzzy numbers (TFNs) [39] are used for fuzzification of crisp input values.

Thus, if $a$ be a crisp input of the proposed method, then it is fuzzified into a TFN as

with the following membership function

3. Proposed methodology

MFIS [34] is a systematic formulation of pre-defined if-then rules which interprets human perceptions. In the proposed method, a MFIS is generated to evaluate the performance of all the states and UTs in India to prevent the pandemic situation of COVID-19. The proposed methodology is described as follows:

Step 1: Selection of input and output parameters

The proposed MFIS consists of five input parameters, viz., $\mathit{Population}\mathit{Density}\left(PD\right)$, $\mathit{Number}of\mathit{Tests}per\mathit{Million}\left(NTM\right)$, $\mathit{Confi}$ $\mathit{rmed}\mathit{Cases}per\mathit{Million}\left(CCM\right)$, $\mathit{Recovery}Rate\left(RR\right)$, and $\mathit{Mortality}Rate\left(MR\right)$ in the respective universe of discourses, $X_1$, $X_2$, $X_3$, $X_4$, and $X_5$ and one output parameter, viz.,

$\mathit{Performance}\mathit{Score}\mathit{against}\mathit{COV\; ID}-19\left(PSC\right)$ in the universe of discourse $Z$. To capture the uncertainties associated with the collected data, the MFs of both input and output variables are represented by TFNs.

Step 2: Development of rule base

The rule base of MFIS characterizes the relationship between the input and output parameters. The $i^{th}(i=1,2,\dots ,m)$ rule of a MFIS [39], [40] is of the following form:

where $F_i^{PD},F_i^{NTM},F_i^{CCM},F_i^{RR},F_i^{MR},and{F}_i^{PSC}$ are TFNs representing the qualitative descriptors of $PD,NTM,CCM,RR,MR,\mathit{and PSC}$, respectively, for the $i^{th}$ rule; $x_i\in X_i(i=1,2,\dots ,m)$, and $z\in Z$.

Here, three linguistic terms are considered corresponding to each input and output parameter. Thus, the rule base contains a total number of $3^5=243$ if-then fuzzy rules by combining all the possible outcomes corresponding to input–output parameters. It is to be noted here that in forming the rules, three input parameters, viz., $PD$, $NTM$, $RR$ are considered as the positive factors and $CCM$, $MR$ are considered as the negative factors for the evaluation of $PSC$.

Step 3: Evaluation of firing strength of each rule

The fuzzy intersection method is performed to evaluate the firing strength, ${\delta }_i$, of the $i^{th}(i=1,2,\dots ,m)$ rule as follows:

where $N^{PD}\left(x_1\right),N^{NTM}\left(x_2\right),N^{CCM}\left(x_3\right),N^{RR}\left(x_4\right),\text{ and}{N}^{MR}\left(x_5\right)$ are the membership grades of the respective fuzzy inputs, $N^{PD},N^{NTM},N^{CCM},N^{RR},and{N}^{MR}$ in the form of TFNs. $F_i^{PD}\left(x_1\right),F_i^{NTM}\left(x_2\right),F_i^{CCM}\left(x_3\right),F_i^{RR}\left(x_4\right),\text{ and}{F}_i^{MR}\left(x_5\right)$ are the membership grades of the corresponding qualitative descriptors, $F_i^{PD},F_i^{NTM},F_i^{CCM},F_i^{RR},and{F}_i^{MR}$ of $PD,NTM,CCM,RR,andMR$, respectively, for the $i^{th}(i=1,2,\dots ,m)$ rule.

Step 4: Derivation of fuzzy output of each rule

The fuzzy output, $PS{C}_i$, for the $i^{th}(i=1,2,\dots ,m)$ rule is derived as follows:

where $F_i^{PSC}\left(z\right)$ is the membership grade of the qualitative descriptor, $F_i^{PSC}$ of the output variable, $PSC$ and $z\in Z$.

Step 5: Aggregation of fuzzy outputs

The fuzzy output, $A{G}^{PSC}$, derived from all the rules are aggregated by operating fuzzy union as

Step 6: Defuzzification of the aggregated output

The aggregated output is defuzzified by operating the centroid of area method which gives the final output, $PSC$, as follows:

where $z_k\in Z$, $k=1,2,\dots ,n$ are $n$ quantization of $Z$.

It is to be mentioned here that the higher value of $PSC$ corresponding to a state or UT signifies the better performance of that state or UT to combat against the pandemic of COVID-19.

The proposed methodology is presented through a flowchart as presented in Fig. 2.

4. Performance score of the states and UTs in India to combat against the pandemic situation of COVID-19

In this section, the performance score of 28 states and 7 UTs in India are assessed by means of their efficiency to tackle the pandemic situation of COVID-19 based on the data available up to 30th November, 2020. As per the census report of India, conducted by National Commission on Population in 2011, and information provided by the Department of MoHFW, Government of India, available on the websites as on 30th November, 2020 [36], [37], the crisp value of $PD$, $NTM$, $CCM$, $RR$, and $MR$ are, respectively, found as 368, 1,06,046, 7,076, 93.81, and 1.45 in India. In the proposed methodology, the MFs of all the linguistic hedges representing the input parameters are defined by keeping those values near the centre point of the middle linguistic term of the respective input parameters. The crisp values of those input variables corresponding to all the states and UTs in India are presented in Fig. 3(a–e).

4.3. Formation of fuzzy rule base

Here, each input and output parameter consists of three linguistic terms. Thus, combining all the possible outcomes corresponding to input–output parameters, the rule base of the proposed MFIS contains a total number of $3^5=243$ if-then fuzzy rules which are presented in Table 2.

Table 2.

Fuzzy rule base.

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5. Results and discussions

It is found from the derived results, as presented in Table 3, that Bihar positioned at the top of the table in case of taking preventive measures against COVID-19 with $PSC$ of 75.7746. In spite of being densely populated state, Bihar, recorded low number of confirmed COVID-19 cases (1959 per million) along with very high recovery rate of 97.15% and low mortality rate of 0.54%. Also, it is the fact that the Assembly election was held in Bihar in October, 2020. So, it might be assumed that the state government of Bihar tightened up the grip over the pandemic of COVID-19; and as a result, a downfall in the active cases and mortality rate of COVID-19 was recorded in Bihar. It is also the fact that the recovery rate from COVID-19 was also found high in Bihar due to the increase of corona-dedicated beds and ICUs in the hospitals.

In Result Table 3, the UT in the western India, Dadra and Nagar Haveli and Daman and Diu, ranks just below the rank of Bihar with $PSC$ of 75.6282. Though, Dadra and Nagar Haveli and Daman and Diu is a densely populated region in India, but the facts behind this ranking might be very low number of confirmed COVID-19 cases (3474 per million) along with highest recovery rate of 99.28% in India and very low mortality rate of 0.06%. The administrative bodies in Dadra and Nagar Haveli and Daman and Diu recognized the people as ‘Corona Warriors’ who have completely maintained the lockdown rules. Also, the administration built up a large number of quarantine centres and got successes in isolating the COVID-19 positive people by performing free and rapid COVID-19 tests.

The first COVID-19 positive case was found in Kerala. The state government of Kerala was performing very significant jobs both in administrative and ground level for fighting against COVID-19 during the complete lockdown periods. As a result, Kerala became one of the most progressing states in India for adapting preventive strategies against COVID-19. But, in the last week of August, Kerala celebrates the festival, called “Onam”; and after that a significant jump in the active COVID-19 cases was found. It is suspected that there might be an effect of celebrating that festival, in which gathering of a large number of people took place. As a result, Kerala faced worse effect of COVID-19 during that time. According to the Result Table 3, Kerala remains at the upper side of the table as rank 3rd position with $PSC$ of 75.5097.

In spite of performing a high number of COVID-19 tests, an average number of confirmed COVID-19 cases (6200 per million) along with very high recovery rate of 96.99% and very low mortality rate of 0.46% were found in a state of eastern India, viz., Assam. Considering those facts, Assam belongs to the upper side of Table 3 as 4th, with $PSC$ of 75.0416. More than 1000 medical teams had been appointed in Assam for screening and monitoring the people having seasonal fevers. Further, the accredited social health activists and multi-purpose workers regularly monitored the home quarantined patients. The Government of Assam launched the mobile app ‘COVASS’ and ‘COVID Suraksha’ to spread the information related with COVID-19 and to monitor the patients who had suggested staying under home quarantine, respectively.

Further, Arunachal Pradesh, another state in the eastern India, also shows good fight against COVID-19 with $PSC$ of 74.6466. Having conducting a high number of COVID-19 tests (2,38,779 per million) by the Government of Arunachal Pradesh, very low mortality rate of 0.33% were found there.

The state, Telangana, had performed a high number of COVID-19 tests. From the collected data, it is seen that this state recorded an average number of COVID-19 cases (7249 per million) along with very high recovery rate of 95.75% and very low mortality rate of 0.54%. Thus, Telangana remains at the top side of Table 3 with $PSC$ of 72.1552. Proper face mask utilization, early detection and isolation of COVID-19 patients might also be the reasons for the improvement of COVID-19 situations in Telangana.

Odisha, situated in the eastern coast of India, is habituated for tackling natural disasters, which are frequently faced by this state. This state had shown its proficiency in managing the pandemic situation of COVID-19 as like other disasters. The $PSC$ for this state is evaluated as 70.9396. From the data it is clear that the state Government of Odisha had performed a high number of COVID-19 tests (1,35,206 per million) to isolate the COVID-19 positive people. As a result, a high recovery rate of 97.78% and low mortality rate of 0.54% were found in Odisha.

Further, the state of Haryana, Jharkhand and Andhra Pradesh had also shown significant performances in controlling the spread of COVID-19 having $PSC$ of 68.0894, 66.9933 and 60.6695, respectively. The input data corresponding to those states reflects higher ranking in the Result Table 3. So, those states and UTs can be considered as role model to others in fighting against the pandemic of COVID-19.

On the contrary, the UT of Ladakh which is situated at the north most region of India has the least $PSC$ of 19.6694 in controlling the spread of COVID-19. Though, this state is least densely populated area of India, but it had a high number of confirmed COVID-19 cases (28,679 per million) with recovery rate of 88.17%. The deficiency of COVID-19 hospitals and medical facilities might be one of the primary reasons for this low scoring.

Sikkim, one of the north-eastern states of India, had also faced the worse effect of COVID-19 pandemic in the recent times having $PSC$ of 21.5425. A large number of health workers were found as COVID-19 positive in Sikkim; and hence, the healthcare sectors of Sikkim faced major shortages of workforce during that times. As a result, high mortality rate of 2.17% were recorded in Sikkim.

The state of Maharashtra, situated in the western part of India, has also scored poor $PSC$ of 21.5832 in controlling the spread of COVID-19. According to the COVID-19 statistics on 30th November, 2020, in Maharashtra around 20% of total COVID-19 positive cases and about 34% of total deceased cases of India were found. The city of Mumbai which is the capital of Maharashtra is also the financial capital of India. People from all the states and UTs in India come to Mumbai for their livelihood; and hence most number of migrated COVID-19 cases are found in Maharashtra. For that reason, it became difficult for Maharashtra to control the spread of COVID-19.

The pandemic situation of COVID-19 is worsening rapidly with an increase of confirmed COVID-19 positive cases (31,042 per million) in Goa, a state on the south-western coast in India. The government and private hospitals faced problems in increasing the beds for COVID-19 patients. As a result, Goa was showing poor performance in controlling the spread of COVID-19 with $PSC$ of 22.5984.

Further, the $PSC$ corresponding to Punjab, Madhya Pradesh, Himachal Pradesh, and Uttarakhand are evaluated low, i.e., those states performed poorly in fighting against COVID-19. So, those states and UTs have lots of works to do for controlling the spread of COVID-19.

The validation of the proposed method is performed in the next section.

6. Validation of the proposed method and sensitivity analysis

To test the validity of the proposed model, Pearson’s correlation coefficient is evaluated between the final results and recovery rate of COVID-19 corresponding to each state and UT, and is presented in Table 4. The value of Pearson’s correlation coefficient is found as 0.4284. The positive value of Pearson’s correlation coefficient establishes the consistency and steadiness of the processes followed in the proposed methodology.

Further, a sensitivity analysis is performed to observe the changes of the values of $PSC$ by varying the tolerance ranges, $l$ of the input arguments in Eq. (1), and is presented in Table 5. It is seen from Table 5 that the proposed estimation is sensitive under the tolerance range, $l$ and preserves the same rank of the provinces as like Table 3 up to $l=0.025$, which is a deviation of 2.5% from the exact values of the inputs. The changes of the values of $PSC$ corresponding to different values of $l$ are presented in Fig. 6.

7. Conclusions

The results in Table 3 show that 14 states and UTs in India have scored less than 50. This is very alarming concern to the Government of India to fight against the pandemic situation of COVID-19. Considering the pandemic situation, the proposed study would become very much helpful to the authorities of both central and state governments to identify the district wise containment zones of COVID-19 within a particular state by evaluating the district wise $PSC$ of that state. The proposed study would also be helpful to the government authorities in starting intra-state, and inter-state public transport services and reopening educational institutes, theatres, cinema halls, museums, etc., in those states and UTs having higher value of $PSC$. The states and UTs having higher $PSC$ score could be considered as role models to others for taking preventive strategies to get rid of from the pandemic situation of COVID-19. Further, the proposed research work might also be helpful for the government authorities of India for performing fair and equitable distribution of the economic packages announced by the central Government of India, and also for supplying medical equipment to the states and UTs having lower $PSC$.

It is clear now from the experiences of last few months that vaccine is the only solution for controlling the spread of COVID-19. But it is also becoming doubtful about the preventive power of the vaccines due to the appearance of different new strains of Corona virus. In fact, the world is now facing the challenges of second wave of COVID-19. In the month of July, 2020 WHO declared that 165 countries sharing up to 60% of the world population had signed an agreement of WHO COVAX plan in purpose of fair distribution of the licenced vaccines of COVID-19. But, for such a highly populated country like India, it might be difficult to organize fair and equitable distribution processes of the COVID-19 vaccines among the people. The proposed methodology would be helpful to build up the strategies for selecting the worse affected districts, states or UTs of India where the vaccines are needed to reach first than others, well in advance. Finally, the proposed research methodology may also be implemented in global interests by evaluating the $PSC$ of each country which is affected by COVID-19 for taking preventive strategies, announcing economic packages, supplying medical equipment and distributing COVID-19 vaccines, etc. From that view point, the developed model is applicable for not only in India but also other countries for evaluation of their current COVID-19 status.

In spite of its usefulness in assessing recent situations and for building up strategies to prevent the spread of COVID-19, there are some potential limitations of the proposed study which are described as follows:

• •

  Although the proposed method possesses the input values as fuzzy numbers, but it is unable to provide the fuzzy outcomes. Other kind of inference system may be used for this purpose.

• •

  Here, the obtained results represent the current status of the provinces due to COVID-19. But, the proposed method is unable to provide any solution to overcome from that pandemic situation.

• •

  The proposed model is designed in the context of pandemic situation of COVID-19 in India. So, direct application of the methodology may not provide accurate results for other countries. However, modifications of the input variables, MFs, rule base may increase the reliability of the proposed method for assessing current pandemic scenario of different parts of a country.

Some future research scopes of this article are summarized as follows:

• –

  In this article, $PSC$ of each state and UT in India is evaluated by generating the MFIS with five input parameters, viz., $PP$, $NTM$, $CCM$, $RR$, and $MR$. But depending on the context of research, some other factors such as daily new cases, serious or critical cases of COVID-19, rate of vaccinations, etc., can be considered as input parameters which may enrich the process of MFIS.

• –

  The proposed methodology can also be applicable in estimating the performance of a country, state or UT against other serious virus affected diseases like mumps, rubella, hepatitis, measles, etc., which will be helpful in taking preventive measures, well in advance.

• –

  The proposed method may also be extended in several branches of fuzzy environments, viz., $q$-rung orthopair fuzzy, intuitionistic fuzzy, Pythagorean fuzzy, and other domains.

Finally, it is expected that the proposed method would be helpful to identify the COVID-19 affected regions and to alarm that zone to take preventive measures, well in advance, so that the spread of COVID-19 can be minimized.

CRediT authorship contribution statement

Bappaditya Ghosh: Software, Formal analysis, Investigation, Data curation, Writing - original draft, Review. Animesh Biswas: Conceptualization, Methodology, Formal analysis, Investigation, Writing - review & editing, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.