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Effect of awareness program on diabetes mellitus: deterministic and stochastic approach

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Abstract

Diabetes mellitus is a silent killer and major public health problem all over the world, but knowledge and awareness about diabetes are insufficient in middle and low-level socioeconomic countries. Awareness plays a vital role in understanding about causes factors of diabetes and its prevention. The world is not completely deterministic as there are biological fluctuations present within the population. With this motivation, we propose and analyze diabetes awareness models with human beings suffering from diabetes mellitus and introducing awareness programs driven by the media in deterministic as well as the stochastic environment. In this work, we investigate the effect of awareness programs on the prevalence of epidemiology of diabetes mellitus. The local stability analysis around the biologically feasible equilibrium point of both the model systems are investigated. The analytical results of the models are verified numerically by taking a set of biologically feasible parameter values. Our study reveals that the prevention of diabetes mellitus in humans may be ensured through an awareness program at the community level.

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References

  1. Shashank, R.J., Das, A.K., Vijay, V.J., Mohan, V.: Challenges in diabetes care in India: sheer numbers, lack of awareness and inadequate control. J. Assoc. Physicians India 56, 443–450 (2008)

    Google Scholar 

  2. Lin, E.H., Katon, W., Von Korff, M., Rutter, C., Simon, G.E., Oliver, M., Ciechanowski, P., Ludman, E.J., Bush, T., Young, B.: Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care 27, 2154–2160 (2004)

    Google Scholar 

  3. Singh, B.M., Prescott, J.J., Guy, R., Walford, S., Murphy, M., Wise, P.H.: Effect of advertising on awareness of symptoms of diabetes among the general public: the British Diabetic Association Study. BMJ 308, 632–636 (1994)

    Google Scholar 

  4. World Health Organisation. Global burden of disease: 2016 update, 2016. http://www.who.int/diabetes/global-report/en/. Accessed on 16 Oct 2019

  5. Atlas D.: International Diabetes Federation. IDF Diabetes Atlas, 7th edn. International Diabetes Federation, Brussels (2015). https://www.oedg.at/pdf/1606_IDF_Atlas_2015_UK.pdf. Accessed on 16 Oct 2019

  6. Jacobs, B.A.: Mathematical model for determining diabetes in cape coast. Afr. J. Agric. Res. 2, 68–73 (2016)

    Google Scholar 

  7. Jung, U., Choi, M.S.: Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int. J. Mol. Sci. 15, 6184–6223 (2014)

    MathSciNet  Google Scholar 

  8. Lefebvre, P., Pierson, A.: Prevention through awareness raising global awareness of diabetes and its complications. Eur. J. Endocrinol. 2, 24–28 (2006)

    Google Scholar 

  9. Isley, W.L., Molitch, M.E.: Type 1 diabetes. J. Clin. Endocrinol. Metab. (2005). https://doi.org/10.1210/jcem.90.1.9996

    Article  Google Scholar 

  10. Ramachandran, A.: Know the signs and symptoms of diabetes. Indian J. Med. Res. 140, 579–581 (2014)

    Google Scholar 

  11. Kharroubi, A.T., Darwish, H.M.: Diabetes mellitus: the epidemic of the century. World J. Diabetes 6, 850–867 (2014)

    Google Scholar 

  12. Olokoba, A.B., Obateru, O.A., Olokoba, L.B.: Type 2 diabetes mellitus: a review of current trends. Oman Med. J. 27, 269–273 (2012)

    Google Scholar 

  13. Wu, Y., Ding, Y., Tanaka, Y., Zhang, W.: Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int. J. Med. Sci. 11, 1185–1200 (2014)

    Google Scholar 

  14. Otero, Y.F., Stafford, J.M., McGuinness, O.P.: Pathway-selective insulin resistance and metabolic disease: the importance of nutrient flux. J. Biol. 289, 20462–204629 (2014)

    Google Scholar 

  15. Ye, J.: Mechanisms of insulin resistance in obesity. Front. Med. 7, 14–24 (2013)

    Google Scholar 

  16. Diabetes Prevention Program (DPP) Research Group: The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care (2002). https://doi.org/10.2337/diacare.25.12.2165

  17. Lindström, J., Louheranta, A., Mannelin, M., Rastas, M., Salminen, V., Eriksson, J., Uusitupa, M., Tuomilehto, J.: The Finnish Diabetes Prevention Study (DPS): lifestyle intervention and 3-year results on diet and physical activity. Diabetes Care 26, 3230–3236 (2003)

    Google Scholar 

  18. Cuff, D.J., Meneilly, G.S., Martin, A., Ignaszewski, A., Tildesley, H.D., Frohlich, J.J.: Effective exercise modality to reduce insulin resistance in women with type 2 diabetes. Diabetes Care 26, 2977–2982 (2003)

    Google Scholar 

  19. Ishii, T., Yamakita, T., Sato, T., Tanaka, S., Fujii, S.: Resistance training improves insulin sensitivity in NIDDM subjects without altering maximal oxygen uptake. Diabetes Care 21, 1353–1355 (1998)

    Google Scholar 

  20. Misra, A., Alappan, N.K., Vikram, N.K., Goel, K., Gupta, N., Mittal, K., Bhatt, S., Luthra, K.: Effect of supervised progressive resistance-exercise training protocol on insulin sensitivity, glycemia, lipids, and body composition in Asian Indians with type 2 diabetes. Diabetes Care 31, 1282–1287 (2008)

    Google Scholar 

  21. Silverstein, J., Klingensmith, G., Copeland, K., Plotnick, L., Kaufman, F., Laffel, L., Deeb, L., Grey, M., Anderson, B., Holzmeister, L.A., Clark, N.: Care of children and adolescents with type 1 diabetes: a statement of the American Diabetes Association. Diabetes Care 28, 186–212 (2005)

    Google Scholar 

  22. Mohan, V., Seedat, Y.K., Pradeepa, R.: The rising burden of diabetes and hypertension in Southeast Asian and African regions: need for effective strategies for prevention and control in primary health care settings. Int. J. Hypertens. 2013, 1–14 (2013)

    Google Scholar 

  23. Bansode, B., Nagarajan, R.: Diabetes: a review of awareness, comorbidities, and quality of life in India. J. Soc. Health Diabetes 2017(5), 77–82 (2017). https://doi.org/10.1055/s-0038-1676248

    Article  Google Scholar 

  24. Wee, H.L., Ho, H.K., Li, S.C.: Public awareness of diabetes mellitus in Singapore. Singap. Med. J. 43, 128–134 (2002)

    Google Scholar 

  25. Boutayeb, A., Chetouani, A., Achouyab, A., Twizell, E.H.: A non-linear population model of diabetes mellitus. J. Appl. Math. Comput. 21, 127–139 (2006)

    MathSciNet  MATH  Google Scholar 

  26. Ajmera, I., Swat, M., Laibe, C., Le Novere, N., Chelliah, V.: The impact of mathematical modeling on the understanding of diabetes and related complications. CPT Pharmacomet. Syst. Pharmacol. 2, 1–14 (2013)

    Google Scholar 

  27. Duun-Henriksen, A.K., Schmidt, S., Røge, R.M., Møller, J.B., Nørgaard, K., Jørgensen, J.B., Madsen, H.: Model identification using stochastic differential equation grey-box models in diabetes. J. Diabetes Sci. Technol. 7, 431–440 (2013)

    Google Scholar 

  28. Mahata, A., Mondal, S.P., Alam, S., Roy, B.: Mathematical model of glucose-insulin regulatory system on diabetes mellitus in fuzzy and crisp environment. Ecol. Genet. Genom. 2, 25–34 (2017)

    Google Scholar 

  29. Pinto, C.M., Carvalho, A.R.: Diabetes mellitus and TB co-existence: clinical implications from a fractional order modelling. Appl. Math. Model. 68, 219–43 (2018)

    MathSciNet  MATH  Google Scholar 

  30. Srivastava, H.M., Shanker, D.R., Jain, M.: A study of the fractional-order mathematical model of diabetes and its resulting complications. Math. METHOD Appl. Sci. 42, 4570–4583 (2019)

    MathSciNet  MATH  Google Scholar 

  31. Rani, P.K., Raman, R., Subramani, S., Perumal, G., Kumaramanickavel, G., Sharma, T.: Knowledge of diabetes and diabetic retinopathy among rural populations in India, and the influence of knowledge of diabetic retinopathy on attitude and practice. Rural Remote Health 8, 838 (2008)

    Google Scholar 

  32. Visser, A., Snoek, F.: Perspectives on education and counseling for diabetes patients. Patient Educ. Couns. 53, 251–255 (2004)

    Google Scholar 

  33. Deepa, M., Bhansali, A., Anjana, R.M., Pradeepa, R., Joshi, S.R., Joshi, P.P., Dhandhania, V.K., Rao, P.V., Subashini, R., Unnikrishnan, R., Shukla, D.K.: Knowledge and awareness of diabetes in urban and rural India: the Indian Council of Medical Research India diabetes study (phase I): Indian Council of Medical Research India diabetes 4. Indian J. Endocr. Metab. 18, 379–385 (2014)

    Google Scholar 

  34. Deeb, L.C.: Diabetes technology during the past 30 years: a lot of changes and mostly for the better. Diabetes Spectr. 21, 78–83 (2008)

    Google Scholar 

  35. Nazar, C.M., Bojerenu, M.M., Safdar, M., Marwat, J.: Effectiveness of diabetes education and awareness of diabetes mellitus in combating diabetes in the United Kingdom; a literature review. J. Nephropharmacol. 5, 110–115 (2016)

    Google Scholar 

  36. Saha, T., Chakrabarti, C.: Stochastic analysis of prey–predator model with stage structure for prey. J. Appl. Math. Comput. 35, 195–209 (2011)

    MathSciNet  MATH  Google Scholar 

  37. Bandyopadhyay, M., Chattopadhyay, J.: Ratio-dependent predator–prey model: effect of environmental fluctuation and stability. Nonlinearity 18, 913–936 (2005)

    MathSciNet  MATH  Google Scholar 

  38. Cantrell, R.S., Cosner, C.: On the dynamics of predator–prey models with the Beddington–DeAngelis functional response. J. Math. Anal. Appl. 257, 206–222 (2001)

    MathSciNet  MATH  Google Scholar 

  39. Cosner, C., DeAngelis, D.L., Ault, J.S., Olson, D.B.: Effects of spatial grouping on the functional response of predators. Theor. Popul. Biol. 56, 65–75 (1999)

    MATH  Google Scholar 

  40. Afanas’ev, V.N., Kolmanovskii, V.B., Nosov, V.R.: Mathematical Theory of Control Systems Design. Kluwer Academic, Dordrecht (1996)

    Google Scholar 

  41. Tripathy, J.P.: Burden and risk factors of diabetes and hyperglycemia in India: findings from the Global Burden of Disease Study 2016. Diabetes Metab. Syndr. Obes. 11, 381–387 (2018)

    Google Scholar 

  42. Perra, N., Balcan, D., Gonçalves, B., Vespignani, A.: Towards a characterization of behavior-disease models. PLoS One 6, e23084 (2011)

    Google Scholar 

  43. Samanta, S., Chattopadhyay, J.: Effect of awareness program in disease outbreak—a slow—fast dynamics. Appl. Math. Comput. 237, 98–109 (2014)

    MathSciNet  MATH  Google Scholar 

  44. Samanta, S., Rana, S., Sharma, A., Misra, A.K., Chattopadhyay, J.: Effect of awareness programs by media on the epidemic outbreaks: a mathematical model. Appl. Math. Comput. 219, 6965–6977 (2013)

    MathSciNet  MATH  Google Scholar 

  45. Baptiste-Roberts, K., Gary, T.L., Beckles, G.L., Gregg, E.W., Owens, M., Porterfield, D., Engelgau, M.M.: Family history of diabetes, awareness of risk factors, and health behaviors among African Americans. Am. J. Public Health 97, 907–912 (2007)

    Google Scholar 

  46. Baranowski, T., Cullen, K.W., Nicklas, T., Thompson, D., Baranowski, J.: Are current health behavioral change models helpful in guiding prevention of weight gain efforts? Obes. Res. 11, 23S–43S (2003)

    Google Scholar 

  47. Kurian, B., Qurieshi, M.A., Ganesh, R., Leelamoni, K.: A community-based study on knowledge of diabetes mellitus among adults in a rural population of Kerala. Int. J. Non-Commun. Dis. 1, 59–64 (2016)

    Google Scholar 

  48. Christie, D., Strange, V., Allen, E., Oliver, S., Wong, I.C., Smith, F., Cairns, J., Thompson, R., Hindmarsh, P., O’Neill, S., Bull, C.: Maximising engagement, motivation and long term change in a Structured Intensive Education Programme in Diabetes for children, young people and their families: Child and Adolescent Structured Competencies Approach to Diabetes Education (CASCADE). BMC Pediatr. 9, 57–67 (2009)

    Google Scholar 

  49. Raman, P.G.: Environmental factors in causation of diabetes mellitus. In: Environmental Health Risk-Hazardous Factors to Living Species, IntechOpen (2016)

  50. Dendup, T., Feng, X., Clingan, S., Astell-Burt, T.: Environmental risk factors for developing type 2 diabetes mellitus: a systematic review. Int. J. Environ. Res. Public Health 15, 78–103 (2018)

    Google Scholar 

  51. Murea, M., Ma, L., Freedman, B.I.: Genetic and environmental factors associated with type 2 diabetes and diabetic vascular complications. Rev. Diabet. Stud. 9, 6–22 (2016)

    Google Scholar 

  52. Rewers, M., Ludvigsson, J.: Environmental risk factors for type 1 diabetes. HHS Public Access 387, 2340–2348 (2016)

    Google Scholar 

  53. Vilar, J.M.G., Rubi, J.M.: Determinants of population responses to environmental fluctuations. Sci. Rep. 8, 887–899 (2007)

    Google Scholar 

  54. Deo, M.G., Pawar, P.V., Kanetkar, S.R., Kakade, S.V.: Prevalence and risk factors of hypertension and diabetes in the Katkari tribe of coastal Maharashtra. J. Postgrad. Med. 63, 106–113 (2017)

    Google Scholar 

  55. Anjana, R.M., Deepa, M., Pradeepa, R., Mahanta, J., Narain, K., Das, H.K., Adhikari, P., Rao, P.V., Saboo, B., Kumar, A., Bhansali, A.: Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMRINDIAB population-based cross-sectional study. Lancet Diabetes Endocrinol. 5, 585–596 (2017)

    Google Scholar 

  56. Sule, B.M., Barakade, A.J.: Growth of population change in Maharashtra (India). Geosci. J. 2, 70–75 (2011)

    Google Scholar 

  57. Upadhyay, R.K., Parshad, R.D., Antwi-Fordjour, K., Quansah, E., Kumari, S.: Global dynamics of stochastic predator–prey model with mutual interference and prey defense. J. Appl. Math. Comput. 60, 169–190 (2019)

    MathSciNet  MATH  Google Scholar 

  58. Al Basir, F., Blyuss, K.B., Ray, S.: Modelling the effects of awareness-based interventions to control the mosaic disease of Jatropha curcas. Ecol. Complex. 36, 92–100 (2018)

    Google Scholar 

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Acknowledgements

The authors are grateful to the anonymous reviewers for their careful reading and valuable comments on the previous version of the paper which help us a lot to improve the manuscript. Saddam Mollah is supported by research fellowship (JRF) from the Council for Scientific and Industrial Research, Government of India (Grant No. 09/096(0894)/2017-EMR-1).

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Correspondence to Santosh Biswas.

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Appendices

Appendix A

Proof of the Theorem 1

The characteristic equation of the system (2.4) at the equilibrium point \({\overline{E}}_{*}({\overline{X}}_{*}, {\overline{S}}_{A*},{\overline{N}}_{*})\) is

$$\rho ^{3}+\alpha _{1}\rho ^{2}+\alpha _{2}\rho +\alpha _{3}=0.$$

By the Routh Hurwitz stability criterion, the equilibrium point \({\overline{E}}_{*}({\overline{X}}_{*}, {\overline{S}}_{A*},{\overline{N}}_{*})\) is asymptotically stable if \(\alpha _{1}>0, \ \alpha _{3}>0\) and \(\alpha _{1}\alpha _{2}-\alpha _{3}>0\).

Now \(\alpha _{1}=-(a_{11}+a_{22}+a_{33})>0\), using the values of \(\{a_{ii}{:}\,i=1,2,3\}\) from (3.3);

$$\begin{aligned} \alpha _{3}= & {} -a_{11}a_{22}a_{33}+a_{12}a_{21}a_{33}-a_{12}a_{31}a_{23}+a_{13}a_{31}a_{22}\\= & {} -a_{11}a_{22}a_{33}+a_{12}a_{21}a_{33}-a_{31}(a_{12}a_{23}-a_{13}a_{22}) \\= & {} -a_{11}a_{22}a_{33}+a_{12}a_{21}a_{33}-a_{31}(\beta \beta _{1}\frac{\lambda {\overline{X}}_{*}}{1+{\overline{X}}_{*}}+\beta \beta _{1}+d\beta )>0, \end{aligned}$$

using the values of \(\{a_{ij}{:}\,i,j=1,2,3\}\) from (3.3).

Here

$$\begin{aligned} \alpha _{1}\alpha _{2}-\alpha _{3}= & {} a_{11}a_{22}(-a_{11}-a_{23})+a_{22}a_{22}(-a_{11}+a_{23})+a_{22}a_{33}(-a_{11}-a_{23})\\&\quad +\,\left[ -a_{11}a_{11}a_{33}+a_{11}a_{12}a_{21}+a_{11}a_{13}a_{31}\right. \\&\left. \quad +\,a_{12}a_{21}a_{22}-a_{11}a_{33}a_{33}+a_{13}a_{31}a_{33}+a_{12}a_{31}a_{23}\right] \\ \end{aligned}$$

Therefore \(-a_{11}-a_{23}=(\beta +d+e-\frac{\lambda {\overline{X}}_{*}}{1+{\overline{X}}_{*}}) >\beta +d+e-\lambda \), as \(0<\frac{\lambda {\overline{X}}_{*}}{1+{\overline{X}}_{*}}<\lambda \),

From the condition \(\beta \beta _{1}+d > \lambda \) and \(0<\beta <1\) imply that \(\beta +d+e-\lambda >0\)

Hence \(-a_{11}-a_{23}>0.\) Again \(a_{11}a_{12}a_{21}=(\beta +d+e)\beta (1-\beta _{1})\frac{(\beta \beta _{1}+d){\overline{S}}_{A*}-\lambda {\overline{X}}_{*}^{2}}{{\overline{X}}_{*}(1+{\overline{X}}_{*})}\) \(>0\), using given conditions.

Similarly, using these conditions and the values of \(\{a_{ij}{:}\,i,j=1,2,3\}\) from (3.3), the other terms within the square bracket are greater than zero.

Finally \(\alpha _{1}\alpha _{2}-\alpha _{3}>0.\)

Hence the equilibrium point \({\overline{E}}_{*}({\overline{X}}_{*}, {\overline{S}}_{A*},{\overline{N}}_{*})\) is asymptotically stable if \(\beta \beta _{1}+d>\lambda \) and \({\overline{S}}_{A*}>{\overline{X}}_{*}^2\). \(\square \)

Appendix B

Proof of the Theorem 3

Let us consider the following positive definite Lyapunov function

$$\begin{aligned}&V(U(t),t)=\frac{1}{2}[\omega _{1}u_{1}^{2}+u_{2}^{2}+\omega _{3}u_{3}^{2} +2\omega _{4}u_{1}u_{3}], \end{aligned}$$
(7.1)

where \(\omega _{i} \ (i=1, 2, 3)\) are real positive constants to be chosen later. It is easy to check that inequalities (5.4) hold true for the Lyapunov function defined in (7.1) with \(\alpha =2\). Furthermore,

$$\begin{aligned} LV(U,t)= & {} (a_{11}u_{1}+a_{12}u_{2}+a_{13}u_{3})\omega _{1}u_{1}+(a_{21}u_{1} +a_{22}u_{2}+a_{23}u_{3})u_{2}\nonumber \\&\quad +(a_{31}u_{1}+a_{32}u_{2}+a_{33}u_{3})\omega _{3}u_{3}\nonumber \\&\quad +(a_{11}u_{1}+a_{12}u_{2}+a_{13}u_{3})\omega _{4}u_{3}+(a_{31}u_{1}+a_{32}u_{2} +a_{33}u_{3})\omega _{4}u_{1}\nonumber \\&\quad + \frac{1}{2}Tr\left[ g^{T}(U)\frac{\partial ^{2}V(U,t)}{\partial U^{2}}g(U)\right] \nonumber \\= & {} \left[ -(\beta +d+e)u_{1}-\beta (1-\beta _{1})u_{2}+\beta u_{3}\right] \omega _{1}u_{1}\nonumber \\&\quad +\left[ \left\{ \frac{(\beta \beta _{1}+d){\overline{S}}_{A*}-\lambda {\overline{X}}_{*}^{2}}{{\overline{X}}_{*}(1+{\overline{X}}_{*})}\right\} u_{1}\nonumber \right. \\&\left. \quad -\left\{ \frac{\lambda {\overline{X}}_{*}}{1+{\overline{X}}_{*}}+ \beta \beta _{1} +d \right\} u_{2}+\frac{\lambda {\overline{X}}_{*}}{1+{\overline{X}}_{*}}u_{3}\right] u_{2} +[-eu_{1}-du_{3}]\omega _{3}u_{3}\nonumber \\&\quad +\left[ -(\beta +d+e)u_{1}-\beta (1-\beta _{1})u_{2}\right. \nonumber \\&\left. \quad +\,\beta u_{3}\right] \omega _{4}u_{3}+[-eu_{1}-du_{3}]\omega _{4}u_{1} +\frac{1}{2}Tr\left[ g^{T}(U)\frac{\partial ^{2}V(U,t)}{\partial U^{2}}g(U)\right] .\nonumber \\ \end{aligned}$$
(7.2)

Now, we find that \(\frac{\partial ^{2}V}{\partial U^{2}}\equiv \) \( \left[ \begin{array}{ccc} \omega _{1} &{}\quad 0 &{}\quad \omega _{4}\\ 0 &{}\quad 1 &{}\quad 0\\ \omega _{4} &{}\quad 0 &{}\quad \omega _{3} \\ \end{array} \right] \).

Therefore, \(g(U(t))^{T}\frac{\partial ^{2}V}{\partial U^{2}}g(U(t))\equiv \) \( \left[ \begin{array}{ccc} \omega _{1}\sigma ^{2}_{1}u^{2}_{1} &{}\quad 0 &{}\quad \omega _{4}\sigma _{1}\sigma _{3}u_{1}u_{3}\\ 0 &{}\quad \sigma ^{2}_{2}u^{2}_{2} &{}\quad 0\\ \omega _{4}\sigma _{1}\sigma _{3}u_{1}u_{3} &{}\quad 0 &{}\quad \omega _{3}\sigma ^{2}_{3}u^{2}_{3} \\ \end{array} \right] \) and hence, \(\frac{1}{2}Tr[g^{T}(U)\frac{\partial ^{2}V(U,t)}{\partial U^{2}}g(U)]=\frac{1}{2}[\omega _{1}\sigma ^{2}_{1}u^{2}_{1}+\sigma ^{2}_{2}u^{2}_{2}+\omega _{3}\sigma ^{2}_{3}u^{2}_{3}]\).

Using this in (5.1) and simplifying, we get

$$\begin{aligned} LV (U,t)&={} -\left[ (\beta +d+e)\omega _{1}+e\omega _{4}-\frac{\sigma ^{2}_{1}}{2} \omega _{1}\right] u^{2}_{1} \nonumber \\&\quad -\left[ \beta (1-\beta _{1})\omega _{1} -\frac{(\beta \beta _{1}+d) {\overline{S}}_{A*}-\lambda {\overline{X}}_{*}^{2}}{{\overline{X}}_{*}(1 +{\overline{X}}_{*})}\right] u_{1}u_{2} \nonumber \\&\quad -\left[ e\omega _{3}-\beta \omega _{1}+(\beta +d+e)\omega _{4}\right] u_{1}u_{3} -\left[ \frac{\lambda {\overline{X}}_{*}}{1+\lambda {\overline{X}}_{*}}+\beta \beta _{1}+d-\frac{\sigma ^{2}_{2}}{2}\right] u^{2}_{2} \nonumber \\&\quad -\left[ \beta (1-\beta _{1})\omega _{4}-\frac{\lambda {\overline{X}}_{*}}{1+\lambda {\overline{X}}_{*}}\right] u_{2}u_{3}-\left[ \left( d-\frac{\sigma ^{2}_{3}}{2}\right) \omega _{3}-\beta \omega _{4}\right] u^{2}_{3}. \nonumber \\ \end{aligned}$$
(7.3)

If we choose \(\omega ^{*}_{1}\), \(\omega ^{*}_{2}\) in such away that

$$\begin{aligned} \beta (1-\beta _{1})\omega _{1}-\frac{(\beta \beta _{1}+d){\overline{S}}_{A*}-\lambda {\overline{X}}_{*}^{2}}{{\overline{X}}_{*}(1+{\overline{X}}_{*})}=0 \ and \ \beta (1-\beta _{1})\omega _{4}-\frac{\lambda {\overline{X}}_{*}}{1+ {\overline{X}}_{*}}=0. \end{aligned}$$

i.e.,

$$\begin{aligned} \omega ^{*}_{1}=\frac{(\beta \beta _{1}+d){\overline{S}}_{A*}- \lambda {\overline{X}}_{*}^{2}}{\beta (1-\beta _{1}){\overline{X}}_{*}(1 +{\overline{X}}_{*})} \ \ and \ \omega ^{*}_{4}=\frac{\lambda {\overline{X}}_{*}}{\beta (1-\beta _{1})(1+{\overline{X}}_{*})}. \end{aligned}$$

Then the Eq. (7.3) becomes

$$\begin{aligned} LV(U,t)&< -\left[ (\beta +d+e)\omega ^{*}_{1}-\frac{\sigma ^{2}_{1}}{2} \omega ^{*}_{1}\right] u^{2}_{1}-\left[ e\omega _{3}-\beta \omega ^{*}_{1}+(\beta +d+e)\omega ^{*}_{4}\right] u_{1}u_{3}\nonumber \\&\quad -\left[ \frac{\lambda {\overline{X}}_{*}}{1+\lambda {\overline{X}}_{*}}+\beta \beta _{1}+d-\frac{\sigma ^{2}_{2}}{2}\right] u^{2}_{2}-\left[ \left( d-\frac{\sigma ^{2}_{3}}{2}\right) \omega _{3}-\beta \omega ^{*}_{4}\right] u^{2}_{3}.\nonumber \\ \end{aligned}$$
(7.4)

Thus, we can write

$$\begin{aligned} LV(U,t)<-u^{T}Qu. \end{aligned}$$
(7.5)

where

$$\begin{aligned} Q\equiv \left[ \begin{array}{ccc} m_{11} &{}\quad m_{12} &{}\quad m_{13}\\ m_{21} &{}\quad m_{22} &{}\quad m_{23}\\ m_{31} &{}\quad m_{32} &{}\quad m_{33}\\ \end{array} \right] \end{aligned}$$

with \(m_{11}=[(\beta +d+e)\omega ^{*}_{1}-\frac{\sigma ^{2}_{1}}{2}\omega ^{*}_{1}]\); \(m_{12}=m_{21}=0\); \(m_{13}=m_{31}=\frac{1}{2}[e\omega _{3}-\beta \omega ^{*}_{1}+(\beta +d+e)\omega ^{*}_{4}]\); \(m_{22}=[\frac{\lambda {\overline{X}}_{*}}{1+\lambda {\overline{X}}_{*}}+\beta \beta _{1}+d-\frac{\sigma ^{2}_{2}}{2}]\); \(m_{23}=m_{32}=0 \); \(m_{33}=[(d-\frac{\sigma ^{2}_{3}}{2})\omega _{3}-\beta \omega ^{*}_{4}]\).

Thus, we have \(m_{ij}\ge 0 \) for \({i, j=1,2,3}\); if the conditions (i) to (iii) of the Theorem 3 are hold. Therefore Q is a real symmetric positive definite matrix and hence all the three eigenvalues \(\lambda _{i}(Q)\) (say) are real positive. Let \(\lambda _{m}=min\{\lambda _{i}(Q),i=1,2,3\}\), then \(\lambda _{m}>0\). Therefore, from inequality (7.5), we get \(LV(u(t))<-\lambda _{m}|u(t)|^{2}\).

Hence the condition (5.5) of Theorem 2 is satisfied. This complete the proof of the theorem. \(\square \)

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Mollah, S., Biswas, S. Effect of awareness program on diabetes mellitus: deterministic and stochastic approach. J. Appl. Math. Comput. 66, 61–86 (2021). https://doi.org/10.1007/s12190-020-01424-6

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