The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation.
A new virus with new and distinctive protein attachment and distribution configurations appeared as a tragedy in Wuhan, China, in December 2019. Following that, the World Health Organization (WHO) proclaimed the virus occurrence a worldwide pandemic in early January 2020, naming the unknown unique virus the Severe Acute Respiratory Syndrome Corona virus (SARS-CoV-2). Furthermore, the disease caused by the virus SARS-CoV-2 is defined using COVID-19 terminology. COVID-19 has been a forerunner of irreversible injury, with an estimated global death toll of WHO. As COVID-19 instances continue to rise, the globe is stimulating for even additional unpredictable times forward, with no vaccination as a preventive strategy and no uniform and conventional therapy or medication approach [
Quarantine and lockdown/shutdown are the only prophylactic measures now proposed for breaking the COVID-19 transmission chain. Though, they are not, a long-term explanation [
Doctors are currently treating COVID-19 patients symptomatically, which means that the drug administered is exclusively based on the patients’ symptoms [
The medical community is dealing with a highly complex problem due to the heterogeneity in curing patients of COVID-19 pandemic due to the lack of a standard therapeutic strategy. To solve and lessen the stated complexity for medical specialists, the authors of this study used a Multi-Criteria Decision Making (MCDM) Methodology [
Furthermore, the study’s analysed findings, which are based on technical validation, a novel knowledge, and methodical classification, will aid the examination team, as well as doctors and medical professionals, in adopting a standardised pharmaceutical approach. The empirical tabulations from this study will make a substantial contribution to healing SARS-CoV-2 sick people and will mark a watershed moment in advanced medicine research using decision-making approaches.
The remainder of this work is organised as follows: the second portion depicts a perspective on past Coronavirus data for comprehension, and the third segment depicts the earlier literature inspection connected to drug cataloguing using a decision-making method. The paper’s fourth segment displays and describes the chosen Coronavirus preventative medications, and the fifth and sixth sections, respectively, define the approved procedure and its numerical assessment. The study’s seventh segment addresses the evaluated results from multiple perspectives, and the paper’s eighth section finishes with a discussion of the paper’s limitations and benefits.
Given the severity of the SARS-CoV-2 crisis, it’s critical to look at COVID-19 statistics and assess the virus’s impact on different countries and regions. According to the WHO [
Weeks (Started from Jan 20, 2020) | America | Europe | Eastern mediterranean | South-East Asia | Africa | |||||
---|---|---|---|---|---|---|---|---|---|---|
Infected | Deaths | Infected | Deaths | Infected | Deaths | Infected | Deaths | Infected | Deaths | |
1 | 6 | 0 | 3 | 0 | 0 | 0 | 4 | 0 | 0 | 0 |
2 | 6 | 0 | 22 | 0 | 5 | 0 | 17 | 0 | 0 | 0 |
3 | 7 | 0 | 14 | 0 | 2 | 0 | 14 | 0 | 0 | 0 |
4 | 3 | 0 | 8 | 2 | 3 | 0 | 2 | 0 | 0 | 0 |
5 | 22 | 0 | 126 | 2 | 48 | 8 | 1 | 0 | 0 | 0 |
6 | 42 | 0 | 1995 | 34 | 685 | 35 | 7 | 1 | 2 | 0 |
7 | 273 | 12 | 10,050 | 381 | 6,225 | 158 | 58 | 0 | 26 | 0 |
8 | 2168 | 35 | 43,403 | 2,112 | 8,393 | 539 | 262 | 6 | 633 | 3 |
9 | 17158 | 205 | 9,800 | 6568 | 9,800 | 993 | 2,281 | 49 | 2,256 | 17 |
10 | 1,01,113 | 1681 | 2,11,749 | 15962 | 20,746 | 1064 | 2,281 | 96 | 2,256 | 32 |
11 | 1,94,916 | 6254 | 2,59,523 | 25517 | 27,170 | 1158 | 4,277 | 160 | 3,372 | 183 |
12 | 2,58,194 | 13344 | 2,59,949 | 28693 | 25,622 | 1122 | 7,920 | 416 | 3,310 | 208 |
13 | 2,47,952 | 21023 | 2,37,534 | 26891 | 29,806 | 932 | 11,930 | 511 | 4,164 | 185 |
14 | 2,72,986 | 18604 | 2,06,291 | 21785 | 36,864 | 965 | 16,617 | 536 | 6,423 | 211 |
15 | 2,89,795 | 22482 | 1,89,900 | 15910 | 40,635 | 983 | 20,479 | 616 | 9,121 | 225 |
16 | 2,96,607 | 16635 | 1,88,977 | 12260 | 56,899 | 1020 | 31,517 | 993 | 13,190 | 305 |
17 | 3,08,497 | 19844 | 1,62,215 | 9763 | 72,185 | 939 | 38,448 | 1027 | 16,037 | 341 |
18 | 3,72,530 | 19849 | 1,36,100 | 7200 | 80,718 | 1072 | 54,036 | 1386 | 18,632 | 363 |
19 | 3,99,480 | 18807 | 1,35,035 | 6061 | 89,195 | 1365 | 69,943 | 1716 | 23,315 | 481 |
20 | 4,73,120 | 20619 | 1,26,003 | 5710 | 1,18,683 | 1973 | 86,527 | 2159 | 30,714 | 594 |
21 | 4,76,893 | 19858 | 1,30,348 | 4346 | 1,34,867 | 2314 | 1,04,897 | 2854 | 36,242 | 850 |
22 | 5,68,086 | 19892 | 1,28,840 | 3931 | 1,38,852 | 3435 | 1,25,094 | 4687 | 49,433 | 876 |
23 | 6,54,118 | 22787 | 1,28,579 | 3468 | 1,26,819 | 3374 | 1,55,321 | 3408 | 61,816 | 911 |
To better understand the SARS-CoV-2 death pattern and ratio, the authors calculated the per week death percentage ratio for each selected location using the data in
Weeks (Started from Jan 20, 2020) | America | Europe | Eastern mediterranean | South-East Asia | Africa |
---|---|---|---|---|---|
1 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
2 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
3 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
4 | 0.00% | 25.00% | 0.00% | 0.00% | 0.00% |
5 | 0.00% | 1.25% | 16.66% | 0.00% | 0.00% |
6 | 0.00% | 1.70% | 5.10% | 14.28% | 0.00% |
7 | 4.40% | 3.79% | 2.54% | 0.00% | 0.00% |
8 | 1.61% | 4.87% | 6.42% | 2.29% | 0.47% |
9 | 1.19% | 67.02% | 10.13% | 2.15% | 0.75% |
10 | 1.66% | 7.54% | 5.13% | 4.21% | 1.42% |
11 | 3.21% | 9.83% | 4.26% | 3.74% | 5.43% |
12 | 5.17% | 11.04% | 4.38% | 5.25% | 6.28% |
13 | 8.48% | 11.32% | 3.13% | 4.28% | 4.44% |
14 | 6.82% | 10.56% | 2.62% | 3.23% | 3.29% |
15 | 7.76% | 8.38% | 2.42% | 3.01% | 2.47% |
16 | 5.61% | 6.49% | 1.79% | 3.15% | 2.31% |
17 | 6.43% | 6.02% | 1.30% | 2.67% | 2.13% |
18 | 5.33% | 5.29% | 1.33% | 2.56% | 1.95% |
19 | 4.71% | 4.49% | 1.53% | 2.45% | 2.06% |
20 | 4.36% | 4.53% | 1.66% | 2.50% | 1.93% |
21 | 4.16% | 3.33% | 1.72% | 2.72% | 2.35% |
22 | 3.50% | 3.05% | 2.47% | 3.75% | 1.77% |
23 | 3.48% | 2.70% | 2.66% | 2.19% | 1.47% |
Furthermore, the linear representation and tabular depiction of data in
Several research studies show that immediate lockdowns/ shutdowns imposed by various governments have not resulted in highly convincing gains in the prevention of SARS-CoV-2. According to a study on the state and impact of lockdown in India, there are no effective benefits of lockdown if the government does not accurately identify, pick, and restrict diseased and susceptible people [
In light of the bleak backdrop described in the preceding paragraph, the current study is motivated by the desire to develop a standard solution for SARS-CoV-2 that will assist doctors and experts in selecting the most effective prognosis for COVID-19 victims in a shorter time frame while keeping the mortality rate low.
The COVID-19 outbreak has been deemed a major public health crisis. Doctors and researchers in the healthcare profession are seeking therapeutical approaches that they believe are best matched to the patient’s physical makeup in order to reduce mortality and boost recovery rates. As a result, treatment for COVID-19 instances has included Allopathy, Unani, and Ayurveda.
We reviewed the numerous studies that cover the courses of therapy for COVID-19 in the proposed study, as well as the opinions of medical professionals on the subject. Following this, a hierarchical model of the therapy procedures has been created that were chosen.
Allopathy is a useful, rapid, and cutting-edge [
Unani medicine is a traditional system of medicine that dates back to the Middle Ages and treats patients with natural medications derived from plants, animals, and minerals. Traditional medical systems are being investigated for providing preventive, supportive, and rehabilitative care to patients in epidemics and pandemics. While there is no direct evidence, certain uncontrolled investigations on traditional medicines show that they may have direct antiviral activity. In their classical Unani writings, under the chapter on influenza, Unani physicians emphasised pandemic (Nazla Wabai/ Nazla Haar) and epidemic infections.
During an epidemic, renowned Unani Scholars advised staying at home and fumigating shelters with aromatic plants such as Ood kham (
Ayurveda is India’s purest and oldest type of therapy, dating back thousands of years. Ayurvedic medicine includes eight different forms of treatment [
The doctors will be able to prescribe a single therapy course for COVID-19 patients based on the success of the treatments. The adopted approach and thorough tabulations are mapped in the next section in order to achieve this goal.
Because numerous therapies for COVID-19 are being tested or researched, it became necessary to use the MCDM tactic to develop valuable selection criteria for mapping the tree structure that would decide the best treatment. In this aspect, the AHP is one of the utmost reliable and tested MCDM strategies. Although comparison matrixes formed during the computation of AHP tactic produce reliable results, many researchers feel that the tactic produces unclear results in certain situations, such as when the number of choices is considerable. We involved the TOPSIS tactic, which delivers more exact results [
Furthermore, in light of the importance of the subject mentioned in the paper, the authors used the hesitant fuzzy tactic in the fuzzy AHP-TOPSIS tactic as a valuable extra tactic for producing precise answers free of any ambiguity and consequences. As a result, the HF-AHP tactic is used to weight the various medications for the COVID-19. After that, the HF-TOPSIS tactic is utilised to choose the best function or alternative for the medications that have been chosen. The complete computation practice has been broken down into 15 steps, which are listed below:
Rank | Abbreviation | Linguistic term | Triangular fuzzy number |
---|---|---|---|
10 | AHI | Absolutely High Importance | (7.0000, 9.0000, 9.0000) |
9 | VHI | Very High Importance | (5.0000, 7.0000, 9.0000) |
8 | ESHI | Essentially High Importance | (3.0000, 5.0000, 7.0000) |
7 | WHI | Weakly High Importance | (1.0000, 3.0000, 5.0000) |
6 | EHI | Equally High Importance | (1.0000, 1.0000, 3.0000) |
5 | EE | Exactly Equal | (1.0000, 1.0000, 1.0000) |
4 | ELI | Equally Low Importance | (0.3300, 1.0000, 1.0000) |
3 | WLI | Weakly Low Important | (0.2000, 0.3300, 1.0000) |
2 | ESLI | Essentially Low Importance | (0.1400, 0.2000, 0.3300) |
1 | VLI | Very Low Importance | (0.1100, 0.1400, 0.2000) |
0 | ALI | Absolutely Low Importance | (0.1100, 0.1100, 0.1400) |
After these estimations, the inspectors want to classify the 1st and 2nd type weights through η, as well as classify the number [0,1], with the help of
Here, from the equations
Thereafter, the value of CR (Consistency Ratio) is assessed via.
In
Following step 9, the HF-TOPSIS approach must be used to identify and find the most suited and best choice. The TOPSIS procedure is one of the most elegant and straightforward methods to selecting and evaluating amazing alternatives for real-world issues [
The other significant stages are distinct as follows:
The calculation of fuzzy function envelop for intake ranks is defined as follows:
Here,
The authors gathered comments and data for each of the COVID-19 treatment described from twenty professionals from all over the healthcare industry in order to enable the adoption and discussion of the HF-AHP-TOPSIS tactic in our setting. Experts were invited to an online meeting app and informed about the pharmacological tree structure tactic for COVID-19 treatment. Expert opinions were gathered in the form of linguistic values, and computations were performed on them, as shown in the preceding section. The data from the experts was also used to create the pair-wise comparison matrix. The professionals obvious to create a fuzzy envelope for level 1 of the tree structure, where authors categorise two treatment paths. The fuzzy envelope values developed for the first level of the tree structure are listed in
M1 | M2 | M3 | |
---|---|---|---|
Allopath (M1) | EE | B/W EHI and WHI | B/W ESHI and VHI |
Unani (M2) | – | EE | B/W WHI and ESHI |
Ayurveda (M3) | – | – | EE |
The CR value was calculated using the supplied scores, and t was confirmed using step 5 and
The fuzzy envelope (D12) was selected as “B/W EHI and WHI”. The Triangular Fuzzy Numbers (TFN) related with the declared linguistic values are (1, 1, 3) and (1, 3, 5), respectively. With the help of
M1 | M2 | M3 | |
---|---|---|---|
Allopath (M1) | 1.000000, 1.000000, 1.000000, 1.000000 | 1.000000, 1.000000, 3.000000, 5.000000 | 0.330000, 1.000000, 1.000000, 3.000000 |
Unani (M2) | 0.200000, 0.330000, 1.000000, 1.000000 | 1.000000, 1.000000, 1.000000, 1.000000 | 0.200000, 0.330000, 1.000000, 1.000000 |
Ayurveda (M3) | 0.330000, 1.000000, 1.000000, 3.000000 | 1.000000, 1.000000, 3.000000, 5.000000 | 1.000000, 1.000000, 1.000000, 1.000000 |
= [(1.000000 X 1.000000 X 0.330000)1/3, (1.000000 X 1.000000 X 1.000000)1/3, (1.000000 X 3.000000 X 1.000000)1/3, (1.000000 X 5.000000 X 3.000000)1/3]
= (0.690000, 1.000000, 1.440000, 2.470000)
Correspondingly, remaining
Correspondingly, remaining
Similarly, defuzzified weights of
Thereafter, normalize the weights by using
=
=1.351600
Similarly, normalized weights of
Geometric means | Fuzzify local weights | Defuzzified weights | Normalized weights | |
---|---|---|---|---|
Allopath (M1) | 0.690000, 1.000000, 1.440000, 2.470000 | 0.120000, 0.260000, 0.580000, 1.430000 | 0.538300 | 0.398300 |
Unani (M2) | 0.340000, 0.480000, 1.000000, 1.000000 | 0.060000, 0.120000, 0.400000, 0.600000 | 0.283300 | 0.209600 |
Ayurveda (M3) | 0.700000, 1.000000, 1.400000, 2.500000 | 0.120000, 0.250000, 0.570000, 1.420000 | 0.530000 | 0.392100 |
The fuzzy local weights through the hierarchy are displayed in
Criteria of level 1
Local weights of level 1
Criteria of level 2
Local weights of level 2
Global weights of level 2
Defuzzified weights
Normalized weights
Ranks
Allopath (M1)
0.120000, 0.260000, 0.580000, 1.430000
Remdesivir [M11]
0.050000, 0.164000, 0.280030, 1.010040
0.006000, 0.042000, 0.165000, 1.447000
0.311000
0.089548
4
Lopinavir/Ritonavir [M12]
0.030045, 0.160056, 0.225006, 0.620000
0.004000, 0.040030, 0.130010, 0.880050
0.206000
0.059315
9
HCQ and Azithromycin [M13]
0.050090, 0.200080, 0.300480, 1.263000
0.000700, 0.050040, 0.200020, 1.800020
0.387000
0.111431
2
Convalescent plasma [M14]
0.060040, 0.200400, 0.420060, 1.210040
0.000080, 0.060020, 0.240080, 1.730020
0.393000
0.113159
1
High-dose IVIg [M15]
0.030030, 0.080060, 0.180010, 0.490080
0.000040, 0.020020, 0.100050, 0.710010
0.162000
0.046646
11
Tocilizumab [M16]
0.040080, 0.150070, 0.270010, 1.020050
0.000060, 0.040000, 0.150070, 1.460020
0.311000
0.089548
5
Siltuximab[M17]
0.030030, 0.120090, 0.210020, 0.780010
0.000040, 0.030030, 0.120030, 1.110040
0.239000
0.068817
8
Mesenchymal Stem Cell [M18]
0.050040, 0.130030, 0.280010, 0.940080
0.006000, 0.030040, 0.160040, 1.350030
0.292000
0.084077
6
Unani (M2)
0.060000, 0.120000, 0.400000, 0.600000
Behi Dana (Cydonia Oblonga) [M21]
0.050020, 0.100590, 0.290070, 1.020050
0.000060, 0.040010, 0.170030, 1.460020
0.316000
0.090988
3
Unnab (Zizyphus Jujuba) [M22]
0.020020, 0.070030, 0.110030, 0.500030
0.000030, 0.019000, 0.066000, 0.718000
0.148000
0.042614
12
Sapistan (Cordia Myxa) [M23]
0.030010, 0.070080, 0.120010, 0.39000
0.002000, 0.010000, 0.040090, 0.220050
0.057000
0.016412
15
Karanjwa (Caesalpinia Bonducella) [M24]
0.149000, 0.276000, 0.723000, 1.509000
0.000090, 0.034000, 0.290020, 0.870030
0.255000
0.073424
7
Ayurveda (M3)
0.120000, 0.250000, 0.570000, 1.420000
Ashwagandha [M31]
0.070060, 0.218000, 0.455000, 1.031000
0.004000, 0.027000, 0.183000, 0.596000
0.170000
0.048949
10
Guduchi [M32]
0.035000, 0.097000, 0.198000, 0.513000
0.000020, 0.012000, 0.080000, 0.297000
0.080000
0.023035
13
Yashtimadhu [M33]
0.031000, 0.078000, 0.121000, 0.39000
0.000020, 0.010000, 0.040090, 0.225000
0.042000
0.012093
16
Peepli [M34]
0.030030, 0.129000, 0.212000, 0.780010
0.004000, 0.030030, 0.120030, 1.114000
0.039000
0.011229
17
Ayush-64 [M35]
0.111900, 0.200060, 0.700030, 1.000090
0.000090, 0.030040, 0.290020, 0.870030
0.065000
0.018716
14
The different ranks, as well as their related weights, are shown in
Criteria / Alternatives | A1 | A2 | A3 | A4 | A5 |
---|---|---|---|---|---|
Remdesivir [M11] | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.450000, 3.180000, 5.180000, 6.250000 |
Lopinavir/Ritonavir [M12] | 0.910000, 2.450000, 4.450000, 5.650000 | 2.450000, 4.270000, 6.270000, 8.650000 | 0.910000, 2.450000, 4.450000, 5.650000 | 2.450000, 4.270000, 6.270000, 8.650000 | 1.910000, 3.730000, 5.730000, 7.510000 |
HCQ and Azithromycin [M13] | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 1.640000, 3.550000, 5.550000, 6.730000 |
Convalescent plasma [M14] | 1.450000, 3.070000, 4.910000, 5.650000 | 0.820000, 2.270000, 4.270000, 6.650000 | 1.450000, 3.070000, 4.910000, 5.650000 | 0.820000, 2.270000, 4.270000, 6.650000 | 2.450000, 4.270000, 6.270000, 8.650000 |
High-dose IVIg [M15] | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 |
Tocilizumab [M16] | 0.820000, 2.270000, 4.270000, 6.650000 | 1.450000, 3.070000, 4.910000, 5.650000 | 0.820000, 2.270000, 4.270000, 6.650000 | 1.450000, 3.070000, 4.910000, 5.650000 | 0.820000, 2.270000, 4.270000, 6.650000 |
Siltuximab [M17] | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 |
Mesenchymal Stem Cell [M18] | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 |
Behi Dana (Cydonia Oblonga) [M21] | 0.910000, 2.450000, 4.450000, 5.650000 | 2.450000, 4.270000, 6.270000, 8.650000 | 0.910000, 2.450000, 4.450000, 5.650000 | 2.450000, 4.270000, 6.270000, 8.650000 | 0.910000, 2.450000, 4.450000, 5.650000 |
Unnab (Zizyphus Jujuba) [M22] | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 |
Sapistan (Cordia Myxa) [M23] | 1.820000, 3.730000, 5.730000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 |
Karanjwa (Caesalpinia Bonducella) [M24] | 0.910000, 2.450000, 4.450000, 5.650000 | 0.910000, 2.450000, 4.450000, 5.650000 | 2.450000, 4.270000, 6.270000, 8.650000 | 0.910000, 2.450000, 4.450000, 5.650000 | 2.450000, 4.270000, 6.270000, 8.650000 |
Ashwagandha [M31] | 2.820000, 4.640000, 6.640000, 8.510000 | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 |
Guduchi [M32] | 1.450000, 3.070000, 4.910000, 5.650000 | 1.450000, 3.070000, 4.910000, 5.650000 | 0.820000, 2.270000, 4.270000, 6.650000 | 1.450000, 3.070000, 4.910000, 5.650000 | 0.820000, 2.270000, 4.270000, 6.650000 |
Yashtimadhu [M33] | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 1.910000, 3.730000, 5.730000, 7.510000 | 2.820000, 4.640000, 6.640000, 8.510000 |
Peepli [M34] | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 | 1.820000, 3.730000, 5.730000, 6.730000 | 0.820000, 2.270000, 4.270000, 6.650000 | 1.450000, 3.070000, 4.910000, 5.650000 |
Ayush-64 [M35] | 0.910000, 2.450000, 4.450000, 5.650000 | 2.450000, 4.270000, 6.270000, 8.650000 | 0.910000, 2.450000, 4.450000, 5.650000 | 1.820000, 3.730000, 5.730000, 6.730000 | 1.640000, 3.550000, 5.550000, 6.730000 |
Alternatives | d+i | d–i | Gap degree of CC+i | Satisfaction degree of CC–i |
---|---|---|---|---|
A1 | 0.045654141 | 0.024002515 | 0.385474445 | 0.645447445 |
A2 | 0.033325647 | 0.047898632 | 0.655654744 | 0.345655874 |
A3 | 0.044457874 | 0.025685974 | 0.387856322 | 0.622362221 |
A4 | 0.031653874 | 0.045658745 | 0.525654449 | 0.455655585 |
A5 | 0.035665599 | 0.045487932 | 0.532235412 | 0.455444447 |
The pandemic is one of the century’s furthermost dangerous outbreaks. According to the study’s material and tactics part, the number of infected patients of COVID-19 disease is rising at an alarming rate in every region of the world. The death ratio of disease is substantially lower in comparison to infection ratio, according to the analysis of the tactic and material portion of this work. For the COVID-19 disease, there is currently no standardised or officially proclaimed treatment [
The authors conducted an examination of numerous therapies provided by doctors treating COVID-19 instances in order to satisfy the study’s objectives. According to the results of the computation practice, “Convalescent Plasma” is the most chosen treatments against the COVID-19. Peepli has the lowest ranking in the calculation, thus we can conclude that it is ineffective in the treatment of patients of COVID-19 disease. This form of computation gives a trustworthy and conclusive reference for specialists who want to confirm the success of various COVID-19 therapy options being pursued around the world. Our findings would also contribute to the scientific community’s COVID contagion research and development efforts.
Furthermore, when conducting a mathematical computation, it is critical to comprehend and analyse the robustness of the computed outcomes [
Tryouts | A1 | A2 | A3 | A4 | A5 | |
---|---|---|---|---|---|---|
Tryout-0 | Satisfaction Degree (CC-i) | 0.6454475 | 0.3456559 | 0.6223622 | 0.4556556 | 0.4554444 |
Tryout-1 | 0.6699784 | 0.5545357 | 0.5232751 | 0.6202751 | 0.5085751 | |
Tryout-2 | 0.6742357 | 0.5615784 | 0.5137751 | 0.6859784 | 0.5050357 | |
Tryout-3 | 0.7292357 | 0.6020751 | 0.5532751 | 0.6589357 | 0.5460784 | |
Tryout-4 | 0.5908751 | 0.4821784 | 0.4482751 | 0.6065784 | 0.4291784 | |
Tryout-5 | 0.5908784 | 0.4855751 | 0.4407764 | 0.5364751 | 0.4290357 | |
Tryout-6 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | |
Tryout-7 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | |
Tryout-8 | 0.6250751 | 0.5311357 | 0.4764751 | 0.5482357 | 0.4659764 | |
Tryout-9 | 0.6449751 | 0.5155764 | 0.4832784 | 0.5769764 | 0.4592357 | |
Tryout-10 | 0.6432784 | 0.5265357 | 0.4847357 | 0.5755357 | 0.4629751 | |
Tryout-11 | 0.6272751 | 0.5025764 | 0.4862764 | 0.5735764 | 0.4702764 | |
Tryout-12 | 0.6196751 | 0.4975357 | 0.4870357 | 0.5725751 | 0.4735751 | |
Tryout-13 | 0.7072784 | 0.7480784 | 0.5647764 | 0.6712751 | 0.5545784 | |
Tryout-14 | 0.5908751 | 0.4821784 | 0.4482751 | 0.6065784 | 0.4291784 | |
Tryout-15 | 0.5908784 | 0.4855751 | 0.4407764 | 0.5364751 | 0.4290357 | |
Tryout-16 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | |
Tryout-17 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 |
The results in
In addition, because the examined outcomes are connected to a very sensitive domain, the authors wished to double-check the results for correctness. To do this, authors used a Marginal Mean analysis and sensitivity analysis,
Experiments/ Alternatives | A1 | A2 | A3 | A4 | A5 | Marginal mean |
---|---|---|---|---|---|---|
Unique Results | 0.6454475 | 0.3456559 | 0.6223622 | 0.4556556 | 0.4554444 | 0.50240200 |
Tryout-0 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.50240800 |
Tryout-1 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.57532700 |
Tryout-2 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.58812000 |
Tryout-3 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.61792000 |
Tryout-4 | 0.5904784 | 0.4855751 | 0.4407764 | 0.5364751 | 0.4290357 | 0.51141700 |
Tryout-5 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.49654800 |
Tryout-6 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.57707900 |
Tryout-7 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.53332800 |
Tryout-8 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.52937900 |
Tryout-9 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.53600800 |
Tryout-10 | 0.5908451 | 0.4874784 | 0.4487851 | 0.6065784 | 0.4277474 | 0.53861200 |
Tryout-11 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.53199600 |
Tryout-12 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.53007900 |
Tryout-13 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.64919700 |
Tryout-14 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.61794500 |
Tryout-15 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.51147400 |
Tryout-16 | 0.6740357 | 0.5651357 | 0.5260751 | 0.6121764 | 0.5079764 | 0.53344700 |
Tryout-17 | 0.6295764 | 0.5272751 | 0.4810784 | 0.5625764 | 0.4661357 | 0.52887400 |
Marginal mean | 0.64636800 | 0.52193000 | 0.51293200 | 0.58070100 | 0.47801000 |
Furthermore, after determining the marginal means for each tryout and the innovative tested outcomes [shown in
Furthermore, we did a comparison analysis of other similar MCDM procedures to reinforce the choice of the adopted methodology and verify its applicability as well as benefits for the current study. The comparison analysis results are explained and displayed in the accompanying
Different MCDM Approaches/Alternatives | A1 | A2 | A3 | A4 | A5 |
---|---|---|---|---|---|
Hesitant-Fuzzy-AHP-TOPSIS | 0.6454475 | 0.3444759 | 0.6223622 | 0.4556556 | 0.4554444 |
Fuzzy-AHP-TOPSIS | 0.6345244 | 0.5346587 | 0.4754574 | 0.5846539 | 0.4784547 |
Fuzzy-Delphi-AHP-TOPSIS | 0.6245474 | 0.5147494 | 0.4454745 | 0.5647457 | 0.4645748 |
Classical-AHP-TOPSIS | 0.6244547 | 0.5444587 | 0.4445955 | 0.5445474 | 0.4648597 |
Delphi-AHP-TOPSIS | 0.6244784 | 0.5454727 | 0.4745874 | 0.5447459 | 0.4645566 |
Because the hesitating circumstance is so widespread during the expert opinion intake process, HFS has been widely used by researchers in earlier years [
The following is a list of our study’s major contributions
The study uses a scientific methodology to assess the efficacy of various coronavirus treatment courses. This is a one-of-a-kind endeavour that will serve as an accurate repository for clinicians who will be able to study our tabulations and choose the best course of treatment for COVID-19 instances. Healthcare experts and researchers are currently befuddled by the lack of a verified and assured treatment/therapy or vaccination against SARS-CoV-2. Our research is based on three key steps: I identifying the 13 most commonly used COVID-19 treatment strategies and proving their efficacy through various research studies involving the treatment of active COVID-19 cases, (ii) gathering doctors’ opinions on those drugs, and (iii) conducting a thorough mathematical analysis to determine the most prioritised course of action. As a result, the scientifically processed and tested outcomes from our study’s decision-making technique will aid in dispelling the ambiguity around the selection of effective treatments. Based on numerical prioritising and efficacy assessment, the paper’s findings reflect and provide a methodical, intelligible pathway for experts of healthcare to determine and choice a suitable treatment. The belief system and logical approach used in this study will prove to be a success for future scientists and can be applied to a variety of fields in the future.
There are also a slew of potential future possibilities linked to this research. As a follow-up to this study, new treatment paths and courses could be added to cover more therapy patterns and provide a large prioritisation list based on effectiveness. Furthermore, after forecasting for future years, this methodology can be used to rank the most contaminated and least infested regions. However, there are a few limitations to this research, which are noted below:
The study summarises and selects just the most extensively utilised and popular SARS-CoV-2 treatment options. However, the authors feel that there are a variety of additional drugs and treatment patterns for COVID-19 patients that are probably in use but are not widely known. The evaluated outcomes in this study were mathematically achieved and evaluated solely on the basis of medical practitioners’ ideas and opinions, but pharmaceuticals and medicines are a sector in which only the element’s actual properties can provide accurate and efficient results. As a result, it’s possible that the produced results aren’t as convincing as they should be in some circumstances and in the view of some experts.
A high infection rate demonstrates the critical need for a SARS-CoV-2 vaccination or a conventional treatment protocol. Doctors have prescribed various courses of treatment according on their calculation of the patients’ situation, but the fight against COVID-19 is far from over. Various medical specialists and governments have offered a number of therapy options. However, the plethora of therapy options for COVID-19 patients causes misunderstanding among doctors and researchers as to which treatments are helpful and which are not. To fill this scientific void, the study aims to present a prioritised list of common treatment courses for two medical paths: Allopathy, Unani, and Ayurveda. The effectiveness of treatments is assessed using a scientific MCDM approach known as Hesitant-Fuzzy-AHP-TOPSIS, which produces accurate and high-quality results (Tested). Furthermore, it is clear from the assessment procedure that Convalescent plasma has the highest priority and Peepli has the lowest, and that all treatment courses are ranked in between these two treatment courses. Furthermore, to improve the quality and efficiency of the acquired results, the authors used various types of analysis such as sensitivity, marginal mean, and comparison analysis. These analyses clearly demonstrate that the study’s assessed results are of high quality (through Sensitivity Analysis), efficiently effective (by Marginal Mean Assessment), and analysed and assessed using the best feasible approach (through Comparison Analysis). As a result, the current study, with its validated, correct, and authentic empirical frame, provides a sound foundation for the scientific community and the medical community to build on. We are convinced that the proposed findings and priority list (Ranks) of various courses of treatment can give medical experts and personnel an idea and efficiently assist them in treating corona patients.
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number “IFPHI: 266-611-2020” and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.