Research Article
BibTex RIS Cite
Year 2023, Volume: 6 Issue: 3, 133 - 149, 21.12.2023
https://doi.org/10.33187/jmsm.1339842

Abstract

References

  • [1] WHO, Obesity and overweight 2021, available at https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
  • [2] F.Q. Nuttall, Body mass index: Obesity, BMI, and health: A Critical review, Nutr. Res., 50(3) (2015), 117-128.
  • [3] M. Akram, Diabetes mellitus type 2: Treatment strategies and options: A review, Diabetes Metab. J., 4(9) (2013), 304-313.
  • [4] A. Golay, J. Ybarr, Link between obesity and type 2 diabetes, Best Pract. Res. Clin. Endocrinol Metab., 19(4) (2005), 649-663.
  • [5] T. Yang, B. Zhao, D. Pei, Evaluation of the Association between obesity markers and type 2 diabetes: A cohort study based on a physical examination population, J. Diabetes Res., 19(4) (2021), Article ID 6503339, 9 pages, doi: 10.1155/2021/6503339.
  • [6] K. Ejima, D. Thomas, D.B. Allison, A Mathematical model for predicting obesity transmission with both genetic and nongenetic heredity, Obesity (Silver Spring), 26(5) (2018), 927–933.
  • [7] S. Kim, So-Yeun Kim, Mathematical modeling for the obesity dynamics with psychological and social factors, East Asian Math. J., 34(3) (2018), 317-330.
  • [8] L.P. Paudel, Mathematical modeling on the obesity dynamics in the Southeastern region and the effect of intervention, Univers. J. Math. Appl., 7(3) (2019), 41-52.
  • [9] S.M. Al-Tuwairqi, R.T. Matbouli, Modeling dynamics of fast food and obesity for evaluating the peer pressure effect and workout impact, Adv. Differ. Equ., 58 (2021), 1-22.
  • [10] S. Bernard, T. Cesar, A. Pietrus, The impact of media coverage on obesity, Contemp. Math., 3(1) (2021), 60-71.
  • [11] R.S. Dubey, P. Goswami, Mathematical model of diabetes and its complications involving fractional operator without singular kernel, Discrete Contin. Dyn. Syst. - Ser. S., 14(7) (2021), 2151–2161.
  • [12] Sandhya, D. Kumar, Mathematical model for Glucose-Insulin regulatory system of diabetes Mellitus, Adv. Appl. Math. Biosci., 2(1) (2011),39-46.
  • [13] S. Anusha, S. Athithan, Mathematical modeling of diabetes and its restrain, Int. J. Mod. Phys. C, 32(9) (2021), 2150114.
  • [14] W. Banzi, I. Kambutse, V. Dusabejambo, E. Rutaganda, F. Minani, J. Niyobuhungiro, L. Mpinganzima, J.M. Ntaganda, Mathematical model for Glucose-Insulin regulatory system of diabetes Mellitus, Int J Math Math. Sci., 2(1) (2021), Article ID 6660177, 12 pages.
  • [15] E. M. D. Moya, A. Pietrus, S. Bernard, Mathematical model for the Study of obesity in a population and its impact on the growth of diabetes, Math. Model. Anal., 28(4) (2023), 611–635.
  • [16] R. Figueiredo Camargo, E. Capelas de Oliveira, (1 Ed.), C´alculo fracion´ario, Livraria da Fısica, Sao Paulo, 2015.
  • [17] H. Kheiri, M. Jafari, Optimal control of a fractional-order model for the HIV/AIDS epidemic, Int. J. Biomath, 11(7) (2018), 1850086, doi:10.1142/S1793524518500869.
  • [18] K. Diethelm, (1 Ed.), The Analysis of Fractional Differential Equations, Springer-Verlag Berlin Heidelberg, 2014.
  • [19] L. Carvalho de Barros et al., The memory effect on fractional calculus: An application in the spread of COVID-19, J. Comput. Appl. Math., 40(3) (2021), 72, doi:10.1007/s40314-021-01456-z.
  • [20] M. Saeedian et al., Memory effects on epidemic evolution: The susceptible-infected-recovered epidemic model, Phys. Rev. E, 95(2) (2017), 022409, doi:10.1103/PhysRevE.95.022409.
  • [21] D. Baleanu, F.A. Ghassabzade, J.J. Nieto, A. Jajarmi, On a new and generalized fractional model for a real cholera outbreak, Alexandria Eng. J., 61(11) (2022), 9175-9186. doi: /10.1016/j.aej.2022.02.054.
  • [22] N. Z. Monteiro and S. R. Mazorque, Fractional derivatives applied to epidemiology, Trends Comput. Appl. Math., 22(2) (2021). doi:10.5540/tcam.2021.022.02.00157.
  • [23] M. Vellappandi, P. Kumar, V. Govindaraj, Role of fractional derivatives in the mathematical modeling of the transmission of Chlamydia in the United States from 1989 to 2019, Nonlinear Dyn., 11 (2023), 4915–4929 doi: 10.1007/s11071-022-08073-3.
  • [24] M. Inc, B. Acay, H. W. Berhe, A. Yusuf, A. Khan, S. Yao, Analysis of novel fractional COVID-19 model with real-life data application, Results Phys., 23 (2021), 103968. doi: 10.1016/j.rinp.2021.103968.
  • [25] K. Diethelm, A fractional calculus based model for the simulation of an outbreak of dengue fever, Nonlinear Dyn., 71 (2013), 613–619, doi: 10.1007/s11071-012-0475-2.
  • [26] E.M.D. Moya, A. Pietrus, S.M. Oliva, Mathematical model with fractional order derivatives for Tuberculosis taking into account its relationship with HIV/AIDS and diabetes, Jambura J. Biomath., 2(2) (2021), 80-95.
  • [27] Z. Odibat, N. Shawagfeh, A fractional calculus based model for the simulation of an outbreak of dengue fever, Appl. Math. Comput. 186(1) (2013), 286-293.
  • [28] C.M.A. Pinto, A.R.M. Carvalho, Diabetes mellitus and TB co-existence: Clinical implications from a fractional order modelling, Appl. Math. Modell., 68 (2019), 219-243.
  • [29] C.M.A. Pinto, A.R.M. Carvalho, The HIV/TB coinfection severity in the presence of TB multi-drug resistant strains, Ecol. Complexity, 32(Part A) (2019), 1-20.
  • [30] W. Lin, Global existence theory and chaos control of fractional differential equations, J. Math. Anal. Appl., 332(1) (2007), 709-726.
  • [31] O. Diekmann, J.A.P. Heesterbeek, M.G. Roberts, The construction of next-generation matrices for compartmental epidemic models, J. R. Soc. Interface, 7(47) (2010), 873-885.
  • [32] P. Van Den Driessche, J. Watmoughs, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29-48.
  • [33] O. Diekmann, J.A.P. Heesterbeek, J.A.J. Metz, On the definition and the computation of the basic reproduction ratio in model for infectious diseases in heterogeneous populations, J. Math. Biol., 28 (1990), 365–382.
  • [34] E. Ahmed, A. M. A. El-Sayed, H. A. A. El-Sakau, Onsome Routh-Hurwitz conditions for fractional order differential equations and their applications in Lorenz, R¨ossler, Chua and Chen systems, Physics Letters A, 358(1) (2006), 1-4.
  • [35] Fatmawati, M. A. Khan, E. Bonyah, Z. Hammouch, E. M. Shaiful, A mathematical model of tuberculosis (TB) transmission with children and adults groups: A fractional model, AIMS Math. 5 (2020), 2813-2842.
  • [36] C. M. A. Pinto, A. R. M. Carvalho, A latency fractional order model for HIV dynamics, J. Comput. Appl. Math. 312 (2017), 240-256.
  • [37] A. R. M. Carvalho, C. M. A. Pinto, D. Baleanu, HIV/HCV coinfection model: a fractional-order perspective for the effect of the HIV viral load, Adv. Differ. Equations. (2018), 1-22.
  • [38] C. Castillo-Chavez, Z. Feng, W. Huang, On the Computation of Â0 and Its Role on Global Stability, In: Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction, Springer-Verlag, New York, (2002), 229-250, doi:10.1007/978-1-4613-0065-6.
  • [39] F.N. Ngoteya, Y. Nkansah-Gyekye, Sensitivity Analysis of Parameters in a Competition Model, Appl. Comput. Math. 4(5) (2015), 363-368.
  • [40] N. Chitnis, J.M. Hyman, J.M. Cushing, Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model, Bull. Math. Biol. 70(5) (2008),1272-96.
  • [41] M. Zamir, G. Zaman, A.S. Alshomrani, Sensitivity Analysis and Optimal Control of Anthroponotic Cutaneous Leishmania, PLoS ONE 11(8) (2016), e0160513, doi: 10.1371/journal.pone.0160513.
  • [42] K. Diethelm, N.J. Ford, A.D. Freed, A Predictor-Corrector Approach for the Numerical Solution of Fractional Differential Equations, Nonlinear Dyn., 29 (2002), 3-22.
  • [43] K. Diethelm, N.J. Ford and A.D. Freed, Detailed Error Analysis for a Fractional Adams Method, Numer Algorithms 36 (2004),31-52.
  • [44] K. Diethelm, A.D. Freed, The FracPECE Subroutine for the Numerical Solution of Differential Equations of Fractional Order, In: Forschung und wissenschaftliches Rechnen 1999 (1998), 57–71.
  • [45] Centers for Disease Control and Prevention. National Center for Health Statistics, About Underlying Cause of Death 1999–2019. CDC WONDER Online Database, available at http://wonder.cdc.gov/ucd-icd10.html
  • [46] Centers for Disease Control and Prevention. National Center for Health Statistics, About Underlying Cause of Death 2020. CDC WONDER Online Database, available at https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf

A mathematical model with fractional order for obesity with positive and negative interactions and its impact on the diagnosis of diabetes

Year 2023, Volume: 6 Issue: 3, 133 - 149, 21.12.2023
https://doi.org/10.33187/jmsm.1339842

Abstract

Overweight and obesity are current problems humankind faces and have serious health consequences because they contribute to diseases such as heart diseases and diabetes. In this paper, we present a mathematical model for the study of overweight and obesity in a population and its impact on the growth of the number of diabetics. For the construction of the model, we take into account social factors and the interactions between different elements of society. We use fractional-order derivatives in the Caputo sense because of the advantages of this type of technique with respect to the memory effect, and it shows different behaviors depending on the fractional order. We find the basic reproduction number and prove the local and global stability of the disease-free equilibrium points. We study the sensitivity index with respect to the basic reproduction number for parameters associated with weight gain due to social pressure and the rate of diagnosis of diabetes not associated with body weight. To validate the model, we perform computational simulations with data extracted from the literature.
We conclude that for higher fractional orders a higher value of the basic reproduction number was reached. We show that at the end of the study for different fractional orders that normal-weight individuals are decreasing, and overweight, obese, and diabetic people are increasing.

References

  • [1] WHO, Obesity and overweight 2021, available at https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
  • [2] F.Q. Nuttall, Body mass index: Obesity, BMI, and health: A Critical review, Nutr. Res., 50(3) (2015), 117-128.
  • [3] M. Akram, Diabetes mellitus type 2: Treatment strategies and options: A review, Diabetes Metab. J., 4(9) (2013), 304-313.
  • [4] A. Golay, J. Ybarr, Link between obesity and type 2 diabetes, Best Pract. Res. Clin. Endocrinol Metab., 19(4) (2005), 649-663.
  • [5] T. Yang, B. Zhao, D. Pei, Evaluation of the Association between obesity markers and type 2 diabetes: A cohort study based on a physical examination population, J. Diabetes Res., 19(4) (2021), Article ID 6503339, 9 pages, doi: 10.1155/2021/6503339.
  • [6] K. Ejima, D. Thomas, D.B. Allison, A Mathematical model for predicting obesity transmission with both genetic and nongenetic heredity, Obesity (Silver Spring), 26(5) (2018), 927–933.
  • [7] S. Kim, So-Yeun Kim, Mathematical modeling for the obesity dynamics with psychological and social factors, East Asian Math. J., 34(3) (2018), 317-330.
  • [8] L.P. Paudel, Mathematical modeling on the obesity dynamics in the Southeastern region and the effect of intervention, Univers. J. Math. Appl., 7(3) (2019), 41-52.
  • [9] S.M. Al-Tuwairqi, R.T. Matbouli, Modeling dynamics of fast food and obesity for evaluating the peer pressure effect and workout impact, Adv. Differ. Equ., 58 (2021), 1-22.
  • [10] S. Bernard, T. Cesar, A. Pietrus, The impact of media coverage on obesity, Contemp. Math., 3(1) (2021), 60-71.
  • [11] R.S. Dubey, P. Goswami, Mathematical model of diabetes and its complications involving fractional operator without singular kernel, Discrete Contin. Dyn. Syst. - Ser. S., 14(7) (2021), 2151–2161.
  • [12] Sandhya, D. Kumar, Mathematical model for Glucose-Insulin regulatory system of diabetes Mellitus, Adv. Appl. Math. Biosci., 2(1) (2011),39-46.
  • [13] S. Anusha, S. Athithan, Mathematical modeling of diabetes and its restrain, Int. J. Mod. Phys. C, 32(9) (2021), 2150114.
  • [14] W. Banzi, I. Kambutse, V. Dusabejambo, E. Rutaganda, F. Minani, J. Niyobuhungiro, L. Mpinganzima, J.M. Ntaganda, Mathematical model for Glucose-Insulin regulatory system of diabetes Mellitus, Int J Math Math. Sci., 2(1) (2021), Article ID 6660177, 12 pages.
  • [15] E. M. D. Moya, A. Pietrus, S. Bernard, Mathematical model for the Study of obesity in a population and its impact on the growth of diabetes, Math. Model. Anal., 28(4) (2023), 611–635.
  • [16] R. Figueiredo Camargo, E. Capelas de Oliveira, (1 Ed.), C´alculo fracion´ario, Livraria da Fısica, Sao Paulo, 2015.
  • [17] H. Kheiri, M. Jafari, Optimal control of a fractional-order model for the HIV/AIDS epidemic, Int. J. Biomath, 11(7) (2018), 1850086, doi:10.1142/S1793524518500869.
  • [18] K. Diethelm, (1 Ed.), The Analysis of Fractional Differential Equations, Springer-Verlag Berlin Heidelberg, 2014.
  • [19] L. Carvalho de Barros et al., The memory effect on fractional calculus: An application in the spread of COVID-19, J. Comput. Appl. Math., 40(3) (2021), 72, doi:10.1007/s40314-021-01456-z.
  • [20] M. Saeedian et al., Memory effects on epidemic evolution: The susceptible-infected-recovered epidemic model, Phys. Rev. E, 95(2) (2017), 022409, doi:10.1103/PhysRevE.95.022409.
  • [21] D. Baleanu, F.A. Ghassabzade, J.J. Nieto, A. Jajarmi, On a new and generalized fractional model for a real cholera outbreak, Alexandria Eng. J., 61(11) (2022), 9175-9186. doi: /10.1016/j.aej.2022.02.054.
  • [22] N. Z. Monteiro and S. R. Mazorque, Fractional derivatives applied to epidemiology, Trends Comput. Appl. Math., 22(2) (2021). doi:10.5540/tcam.2021.022.02.00157.
  • [23] M. Vellappandi, P. Kumar, V. Govindaraj, Role of fractional derivatives in the mathematical modeling of the transmission of Chlamydia in the United States from 1989 to 2019, Nonlinear Dyn., 11 (2023), 4915–4929 doi: 10.1007/s11071-022-08073-3.
  • [24] M. Inc, B. Acay, H. W. Berhe, A. Yusuf, A. Khan, S. Yao, Analysis of novel fractional COVID-19 model with real-life data application, Results Phys., 23 (2021), 103968. doi: 10.1016/j.rinp.2021.103968.
  • [25] K. Diethelm, A fractional calculus based model for the simulation of an outbreak of dengue fever, Nonlinear Dyn., 71 (2013), 613–619, doi: 10.1007/s11071-012-0475-2.
  • [26] E.M.D. Moya, A. Pietrus, S.M. Oliva, Mathematical model with fractional order derivatives for Tuberculosis taking into account its relationship with HIV/AIDS and diabetes, Jambura J. Biomath., 2(2) (2021), 80-95.
  • [27] Z. Odibat, N. Shawagfeh, A fractional calculus based model for the simulation of an outbreak of dengue fever, Appl. Math. Comput. 186(1) (2013), 286-293.
  • [28] C.M.A. Pinto, A.R.M. Carvalho, Diabetes mellitus and TB co-existence: Clinical implications from a fractional order modelling, Appl. Math. Modell., 68 (2019), 219-243.
  • [29] C.M.A. Pinto, A.R.M. Carvalho, The HIV/TB coinfection severity in the presence of TB multi-drug resistant strains, Ecol. Complexity, 32(Part A) (2019), 1-20.
  • [30] W. Lin, Global existence theory and chaos control of fractional differential equations, J. Math. Anal. Appl., 332(1) (2007), 709-726.
  • [31] O. Diekmann, J.A.P. Heesterbeek, M.G. Roberts, The construction of next-generation matrices for compartmental epidemic models, J. R. Soc. Interface, 7(47) (2010), 873-885.
  • [32] P. Van Den Driessche, J. Watmoughs, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29-48.
  • [33] O. Diekmann, J.A.P. Heesterbeek, J.A.J. Metz, On the definition and the computation of the basic reproduction ratio in model for infectious diseases in heterogeneous populations, J. Math. Biol., 28 (1990), 365–382.
  • [34] E. Ahmed, A. M. A. El-Sayed, H. A. A. El-Sakau, Onsome Routh-Hurwitz conditions for fractional order differential equations and their applications in Lorenz, R¨ossler, Chua and Chen systems, Physics Letters A, 358(1) (2006), 1-4.
  • [35] Fatmawati, M. A. Khan, E. Bonyah, Z. Hammouch, E. M. Shaiful, A mathematical model of tuberculosis (TB) transmission with children and adults groups: A fractional model, AIMS Math. 5 (2020), 2813-2842.
  • [36] C. M. A. Pinto, A. R. M. Carvalho, A latency fractional order model for HIV dynamics, J. Comput. Appl. Math. 312 (2017), 240-256.
  • [37] A. R. M. Carvalho, C. M. A. Pinto, D. Baleanu, HIV/HCV coinfection model: a fractional-order perspective for the effect of the HIV viral load, Adv. Differ. Equations. (2018), 1-22.
  • [38] C. Castillo-Chavez, Z. Feng, W. Huang, On the Computation of Â0 and Its Role on Global Stability, In: Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction, Springer-Verlag, New York, (2002), 229-250, doi:10.1007/978-1-4613-0065-6.
  • [39] F.N. Ngoteya, Y. Nkansah-Gyekye, Sensitivity Analysis of Parameters in a Competition Model, Appl. Comput. Math. 4(5) (2015), 363-368.
  • [40] N. Chitnis, J.M. Hyman, J.M. Cushing, Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model, Bull. Math. Biol. 70(5) (2008),1272-96.
  • [41] M. Zamir, G. Zaman, A.S. Alshomrani, Sensitivity Analysis and Optimal Control of Anthroponotic Cutaneous Leishmania, PLoS ONE 11(8) (2016), e0160513, doi: 10.1371/journal.pone.0160513.
  • [42] K. Diethelm, N.J. Ford, A.D. Freed, A Predictor-Corrector Approach for the Numerical Solution of Fractional Differential Equations, Nonlinear Dyn., 29 (2002), 3-22.
  • [43] K. Diethelm, N.J. Ford and A.D. Freed, Detailed Error Analysis for a Fractional Adams Method, Numer Algorithms 36 (2004),31-52.
  • [44] K. Diethelm, A.D. Freed, The FracPECE Subroutine for the Numerical Solution of Differential Equations of Fractional Order, In: Forschung und wissenschaftliches Rechnen 1999 (1998), 57–71.
  • [45] Centers for Disease Control and Prevention. National Center for Health Statistics, About Underlying Cause of Death 1999–2019. CDC WONDER Online Database, available at http://wonder.cdc.gov/ucd-icd10.html
  • [46] Centers for Disease Control and Prevention. National Center for Health Statistics, About Underlying Cause of Death 2020. CDC WONDER Online Database, available at https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
There are 46 citations in total.

Details

Primary Language English
Subjects Modelling and Simulation, Applied Mathematics (Other)
Journal Section Articles
Authors

Erick Manuel Delgado Moya 0000-0001-5937-5374

Alain Pietrus This is me 0009-0005-8064-4950

S´everine Bernard This is me 0000-0001-8493-9821

Silvere Paul nuiro This is me 0000-0002-9537-7913

Early Pub Date December 7, 2023
Publication Date December 21, 2023
Submission Date August 10, 2023
Acceptance Date November 30, 2023
Published in Issue Year 2023 Volume: 6 Issue: 3

Cite

APA Delgado Moya, E. M., Pietrus, A., Bernard, S., Paul nuiro, S. (2023). A mathematical model with fractional order for obesity with positive and negative interactions and its impact on the diagnosis of diabetes. Journal of Mathematical Sciences and Modelling, 6(3), 133-149. https://doi.org/10.33187/jmsm.1339842
AMA Delgado Moya EM, Pietrus A, Bernard S, Paul nuiro S. A mathematical model with fractional order for obesity with positive and negative interactions and its impact on the diagnosis of diabetes. Journal of Mathematical Sciences and Modelling. December 2023;6(3):133-149. doi:10.33187/jmsm.1339842
Chicago Delgado Moya, Erick Manuel, Alain Pietrus, S´everine Bernard, and Silvere Paul nuiro. “A Mathematical Model With Fractional Order for Obesity With Positive and Negative Interactions and Its Impact on the Diagnosis of Diabetes”. Journal of Mathematical Sciences and Modelling 6, no. 3 (December 2023): 133-49. https://doi.org/10.33187/jmsm.1339842.
EndNote Delgado Moya EM, Pietrus A, Bernard S, Paul nuiro S (December 1, 2023) A mathematical model with fractional order for obesity with positive and negative interactions and its impact on the diagnosis of diabetes. Journal of Mathematical Sciences and Modelling 6 3 133–149.
IEEE E. M. Delgado Moya, A. Pietrus, S. Bernard, and S. Paul nuiro, “A mathematical model with fractional order for obesity with positive and negative interactions and its impact on the diagnosis of diabetes”, Journal of Mathematical Sciences and Modelling, vol. 6, no. 3, pp. 133–149, 2023, doi: 10.33187/jmsm.1339842.
ISNAD Delgado Moya, Erick Manuel et al. “A Mathematical Model With Fractional Order for Obesity With Positive and Negative Interactions and Its Impact on the Diagnosis of Diabetes”. Journal of Mathematical Sciences and Modelling 6/3 (December 2023), 133-149. https://doi.org/10.33187/jmsm.1339842.
JAMA Delgado Moya EM, Pietrus A, Bernard S, Paul nuiro S. A mathematical model with fractional order for obesity with positive and negative interactions and its impact on the diagnosis of diabetes. Journal of Mathematical Sciences and Modelling. 2023;6:133–149.
MLA Delgado Moya, Erick Manuel et al. “A Mathematical Model With Fractional Order for Obesity With Positive and Negative Interactions and Its Impact on the Diagnosis of Diabetes”. Journal of Mathematical Sciences and Modelling, vol. 6, no. 3, 2023, pp. 133-49, doi:10.33187/jmsm.1339842.
Vancouver Delgado Moya EM, Pietrus A, Bernard S, Paul nuiro S. A mathematical model with fractional order for obesity with positive and negative interactions and its impact on the diagnosis of diabetes. Journal of Mathematical Sciences and Modelling. 2023;6(3):133-49.

29237    Journal of Mathematical Sciences and Modelling 29238

                   29233

Creative Commons License The published articles in JMSM are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.