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Stability of a delayed SIR epidemic model by introducing two explicit treatment classes along with nonlinear incidence rate and Holling type treatment. (English) Zbl 1438.34294

Summary: In this article, we analyze the stability of a time-delayed susceptible-infected-recovered (S-I-R) epidemic model by introducing two explicit treatment classes (or compartments) along with nonlinear incidence rate. The treatment classes are named as a pre-treated class (\(T_1\)) and post-treated class (\(T_2\)). The pre-treatment and post-treatment rates are being considered as Holling type I and Holling type III, respectively. Long-term qualitative analysis has been carried out after incorporating incubation time delay (\(\tau\)) into the incidence rate. The model analysis shows that the model has two equilibrium points, named as disease-free equilibrium (DFE) and endemic equilibrium (EE). The disease-free equilibrium is locally asymptotically stable when the basic reproduction number (\(R_0\)) is less than one and unstable when \( R_{0} \) is greater than one for time lag \( \tau \ge 0 \), and when \( R_{0} = 1 \) by Castillo-Chavez and Song theorem, the disease-free equilibrium changes its stability from stable to unstable and the model exhibits transcritical bifurcation. Furthermore, some conditions for stability of the endemic equilibrium are obtained. Finally, numerical simulations are presented to exemplify the analytical studies.

MSC:

34K60 Qualitative investigation and simulation of models involving functional-differential equations
92D30 Epidemiology
34K20 Stability theory of functional-differential equations
34K21 Stationary solutions of functional-differential equations
34K25 Asymptotic theory of functional-differential equations
34K18 Bifurcation theory of functional-differential equations
Full Text: DOI

References:

[1] Adebimpe O, Bashiru KA, Ojurongbe TA (2015) Stability analysis of an SIR epidemic model with non-linear incidence rate and treatment. Open J Model Simul 3:104-110 · doi:10.4236/ojmsi.2015.33011
[2] Anderson RM, May RM (1991) Infectious disease of humans. Oxford University Press Inc, New York
[3] Capasso V, Serio G (1978) A generalization of the Kermack-Mckendrick deterministic epidemic model. Math Biosci 42:41-61 · Zbl 0398.92026 · doi:10.1016/0025-5564(78)90006-8
[4] Castillo-Chavez C, Song B (2004) Dynamical models of tuberculosis and their applications. Math Biosci Eng 1:361-404 · Zbl 1060.92041 · doi:10.3934/mbe.2004.1.361
[5] Dubey B, Patra A, Srivastava PK, Dubey US (2013) Modeling and analysis of a SEIR model with different types of nonlinear treatment rates. J Biol Syst 21(3):1350023 · Zbl 1342.92235 · doi:10.1142/S021833901350023X
[6] Dubey B, Dubey P, Dubey US (2015) Dynamics of n SIR model with nonlinear incidence and treatment rate. Appl Appl Math 10(2):718-737 · Zbl 1331.34085
[7] Dubey, Preeti; Dubey, Balram; Dubey, Uma S., An SIR Model with Nonlinear Incidence Rate and Holling Type III Treatment Rate, 63-81 (2016), New Delhi · Zbl 1367.92117 · doi:10.1007/978-81-322-3640-5_4
[8] Gumel AB, Mccluskey CC, Watmough J (2006) An SVEIR model for assessing the potential impact of an imperfect anti-SARS vaccine. Math Biosci Eng 3:485-494 · Zbl 1092.92039 · doi:10.3934/mbe.2006.3.3i
[9] Karim SAA, Razali R (2011) A proposed mathematical model of influenza A, H1N1 for Malaysia. J Appl Sci 11(8):1457-1460
[10] Kumar A, Nilam (2018a) Stability of a time delayed SIR epidemic model along with nonlinear incidence rate and Holling type II treatment rat. Int J Comput Methods 15(6):1850055 · Zbl 1404.34082 · doi:10.1142/S021987621850055X
[11] Kumar A, Nilam (2018b) Dynamical model of epidemic along with time delay; Holling type II incidence rate and monod—Haldane treatment rate. Differ Equ Dyn Syst. https://doi.org/10.1007/s12591-018-0424-8 · Zbl 1404.34082 · doi:10.1007/s12591-018-0424-8
[12] Michael YL, Graef JR, Wang L, Karsai J (1999) Global dynamics of a SEIR model with varying total population size. Math Biosci 160:191-213 · Zbl 0974.92029 · doi:10.1016/S0025-5564(99)00030-9
[13] Sastry S (1999) Analysis, stability and control. Springer, New York · Zbl 0924.93001
[14] Tipsri S, Chinviriyasit W (2014) Stability analysis of SEIR model with saturated incidence and time delay. Int J Appl Phys Math 4(1):42-45 · doi:10.7763/IJAPM.2014.V4.252
[15] van den Driessche P, Watmough J (2002) Reproduction numbers and sub-threshold endemic equilibria for compartment models of disease transmission. Math Biosci 180:29-48 · Zbl 1015.92036 · doi:10.1016/S0025-5564(02)00108-6
[16] Wang X (2004) A simple proof of Descartes’s rule of signs. Am Math Mon. https://doi.org/10.2307/4145072 · Zbl 1080.26507 · doi:10.2307/4145072
[17] Wang W, Ruan S (2004) Bifurcation in an epidemic model with constant removal rates of the infective. J Math Anal Appl 21:775-793 · Zbl 1054.34071 · doi:10.1016/j.jmaa.2003.11.043
[18] Xu R, Ma Z (2009) Stability of a delayed SIRS epidemic model with a nonlinear incidence rate. Chaos Solutions Fract 41:2319-2325 · Zbl 1198.34098 · doi:10.1016/j.chaos.2008.09.007
[19] Zhang Z, Suo S (2010) Qualitative analysis of a SIR epidemic model with saturated treatment rate. J Appl Math Comput 34:177-194 · Zbl 1211.34062 · doi:10.1007/s12190-009-0315-9
[20] Zhou L, Fan M (2012) Dynamics of a SIR epidemic model with limited medical resources revisited. Nonlinear Anal RWA 13:312-324 · Zbl 1238.37041 · doi:10.1016/j.nonrwa.2011.07.036
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