Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccination
The COVID-19 is contagious and menacing, it is more obscure in identifying the infections in primary stage as it able to thrive in both hot and cold conditions. So, in this paper we have developed the Susceptible Vaccinated Infected Recovered (SVIR) model utilising the Michaelis-Menten Functional Re...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019590 |
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| author | G.M. Vijayalakshmi R. Vikram Ali Akgül Murad Khan Hassani |
| author_facet | G.M. Vijayalakshmi R. Vikram Ali Akgül Murad Khan Hassani |
| author_sort | G.M. Vijayalakshmi |
| collection | DOAJ |
| description | The COVID-19 is contagious and menacing, it is more obscure in identifying the infections in primary stage as it able to thrive in both hot and cold conditions. So, in this paper we have developed the Susceptible Vaccinated Infected Recovered (SVIR) model utilising the Michaelis-Menten Functional Response to explain the infections of COVID-19 especially for diabetic who are having more chances of getting infected as they are already vulnerable due to their lack of immunity. We also considered non-diabetic patients and discussed the symptomatic and asymptomatic infections for both diabetic and non-diabetic patients as well as the recuperation from COVID-19 (Coronavirus Disease). We have found the uncertainty in the rate of change variables, so we used the fuzzy concept to lower the uncertainty and find a better model. For this environment, we have used Trapezoidal Numbers(TFN) and fuzzified the rate of change variables and by using the Partition Defuzzification Method(PDM) we defuzzified the values and got accurate results. Additionally, using Python programming, we compared the variations between the crisp and fuzzy models and we also discussed the equilibrium points, performed sensitivity analysis, and examined the stability of the model. |
| format | Article |
| id | doaj-art-983a2e906eeb41b295a45967ef0b8c00 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-983a2e906eeb41b295a45967ef0b8c002025-08-20T02:44:28ZengElsevierResults in Engineering2590-12302025-09-012710588810.1016/j.rineng.2025.105888Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccinationG.M. Vijayalakshmi0R. Vikram1Ali Akgül2Murad Khan Hassani3Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Tamil Nadu, India 600062; Corresponding authors.Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Tamil Nadu, India 600062; Department of Mathematics, DRBCCC Hindu College, Pattabiram, Tamil Nadu, India 600072Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India; Siirt University, Art and Science Faculty, Department of Mathematics, 56100 Siirt, Turkey; Department of Computer Engineering, Biruni University, 34010 Topkapı, Istanbul, Turkey; Near East University, Mathematics Research Center, Department of Mathematics, Near East Boulevard, PC: 99138, Nicosia/Mersin 10, Turkey; Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan; Corresponding authors.Ghazni University, Department of Mathematics, Ghazni, Afghanistan; Corresponding authors.The COVID-19 is contagious and menacing, it is more obscure in identifying the infections in primary stage as it able to thrive in both hot and cold conditions. So, in this paper we have developed the Susceptible Vaccinated Infected Recovered (SVIR) model utilising the Michaelis-Menten Functional Response to explain the infections of COVID-19 especially for diabetic who are having more chances of getting infected as they are already vulnerable due to their lack of immunity. We also considered non-diabetic patients and discussed the symptomatic and asymptomatic infections for both diabetic and non-diabetic patients as well as the recuperation from COVID-19 (Coronavirus Disease). We have found the uncertainty in the rate of change variables, so we used the fuzzy concept to lower the uncertainty and find a better model. For this environment, we have used Trapezoidal Numbers(TFN) and fuzzified the rate of change variables and by using the Partition Defuzzification Method(PDM) we defuzzified the values and got accurate results. Additionally, using Python programming, we compared the variations between the crisp and fuzzy models and we also discussed the equilibrium points, performed sensitivity analysis, and examined the stability of the model.http://www.sciencedirect.com/science/article/pii/S2590123025019590SVIR modelCOVID-19TFNPDMStabilityDiabetics |
| spellingShingle | G.M. Vijayalakshmi R. Vikram Ali Akgül Murad Khan Hassani Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccination Results in Engineering SVIR model COVID-19 TFN PDM Stability Diabetics |
| title | Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccination |
| title_full | Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccination |
| title_fullStr | Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccination |
| title_full_unstemmed | Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccination |
| title_short | Fuzzy mathematical modelling on diabetic and non-diabetic cases after vaccination |
| title_sort | fuzzy mathematical modelling on diabetic and non diabetic cases after vaccination |
| topic | SVIR model COVID-19 TFN PDM Stability Diabetics |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025019590 |
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