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The numerical approximation to a stochastic age-structured HIV/AIDS model with nonlinear incidence rates. (English) Zbl 1492.92109

Summary: In this paper, a stochastic age-structured HIV/AIDS model with nonlinear incidence rates is proposed. It is of great importance to develop efficient numerical approximation methods to solve this HIV/AIDS model since most stochastic partial differential equations (SPDEs) cannot be solved analytically. From the perspective of biological significance, the exact solution of the HIV/AIDS model must be nonnegative and bounded. Then a modified explicit Euler-Maruyama (EM) scheme is constructed based on a projection operator. The EM scheme could preserves the nonnegativity of the numerical solutions and also make the numerical solutions not outside the domain of the exact solutions. The convergence results between the numerical solutions and the exact solutions are analyzed, and some numerical examples are given to verify our theoretical results.

MSC:

92D30 Epidemiology
60H35 Computational methods for stochastic equations (aspects of stochastic analysis)
65C20 Probabilistic models, generic numerical methods in probability and statistics
Full Text: DOI

References:

[1] R. M. Anderson, G. F. Medley, R. M. May and A. M. Johnson, A preliminary study of the transmission dynamics of the human immunodeficiency virus (HIV), the causative agent of AIDS, IMA J. Math. Appl. Med. Biol. 3 (1986), no. 4, 229-263. · Zbl 0609.92025
[2] S. Aniţa, Analysis and Control of Age-Dependent Population Dynamics, Kluwer, Dordrecht, 2000. · Zbl 0960.92026
[3] F. Brauer and C. Castillo-Chavez, Lecture 8: Models for the transmission dynamics of HIV, Mathematical Models for Communicable Diseases, ACM, New York (2012), 163-189.
[4] S. Busenberg and C. Castillo-Chavez, A general solution of the problem of mixing of subpopulations and its application to risk- and age-structured epidemic models for the spread of AIDS, Eur. J. Oral. Sci. 117 (1991), no. 2, 200-203. · Zbl 0764.92017
[5] L. Cai, X. Li and J. Yu, Analysis of a delayed HIV/AIDS epidemic model with saturation incidence, J. Appl. Math. Comput. 27 (2008), no. 1-2, 365-377. · Zbl 1147.92314
[6] V. Capasso and G. Serio, A generalization of the Kermack-McKendrick deterministic epidemic model, Math. Biosci. 42 (1978), no. 1-2, 43-61. · Zbl 0398.92026
[7] J.-F. Chassagneux, A. Jacquier and I. Mihaylov, An explicit Euler scheme with strong rate of convergence for financial SDEs with non-Lipschitz coefficients, SIAM J. Financial Math. 7 (2016), no. 1, 993-1021. · Zbl 1355.60072
[8] N. Dalal, D. Greenhalgh and X. Mao, A stochastic model of AIDS and condom use, J. Math. Anal. Appl. 325 (2007), no. 1, 36-53. · Zbl 1101.92037
[9] Y. Ding, M. Xu and L. Hu, Asymptotic behavior and stability of a stochastic model for AIDS transmission, Appl. Math. Comput. 204 (2008), no. 1, 99-108. · Zbl 1152.92020
[10] Y. Emvudu, D. Bongor and R. Koïna, Mathematical analysis of HIV/AIDS stochastic dynamic models, Appl. Math. Model. 40 (2016), no. 21-22, 9131-9151. · Zbl 1480.92195
[11] A. Gray, D. Greenhalgh, L. Hu, X. Mao and J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math. 71 (2011), no. 3, 876-902. · Zbl 1263.34068
[12] H. W. Hethcote, The mathematics of infectious diseases, SIAM Rev. 42 (2000), no. 4, 599-653. · Zbl 0993.92033
[13] W. Z. Huang, K. L. Cooke and C. Castillo-Chavez, Stability and bifurcation for a multiple-group model for the dynamics of HIV/AIDS transmission, SIAM J. Appl. Math. 52 (1992), no. 3, 835-854. · Zbl 0769.92023
[14] M. Hutzenthaler, A. Jentzen and P. E. Kloeden, Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients, Ann. Appl. Probab. 22 (2012), no. 4, 1611-1641. · Zbl 1256.65003
[15] J. Hyman, J. Li and E. A. Stanley, Threshold conditions for the spread of the HIV infection in age-structured populations of homosexual men, J. Theoret. Biol. 166 (1994), no. 1, 9-31.
[16] H. Inaba, Endemic threshold results in an age-duration-structured population model for HIV infection, Math. Biosci. 201 (2006), no. 1-2, 15-47. · Zbl 1094.92053
[17] C. Ji, D. Jiang, Q. Yang and N. Shi, Dynamics of a multigroup SIR epidemic model with stochastic perturbation, Automatica J. IFAC 48 (2012), no. 1, 121-131. · Zbl 1244.93154
[18] P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, Appl. Math. (New York) 23, Springer, Berlin, 1999. · Zbl 0701.60054
[19] Y. Liang, D. Greenhalgh and X. Mao, A stochastic differential equation model for the spread of HIV amongst people who inject drugs, Comput. Math. Methods Med. 11 (2016), 1-14. · Zbl 1348.92156
[20] W. Liu and X. Mao, Strong convergence of the stopped Euler-Maruyama method for nonlinear stochastic differential equations, Appl. Math. Comput. 223 (2013), 389-400. · Zbl 1329.65018
[21] W. M. Liu, H. W. Hethcote and S. A. Levin, Dynamical behavior of epidemiological models with nonlinear incidence rates, J. Math. Biol. 25 (1987), no. 4, 359-380. · Zbl 0621.92014
[22] W. M. Liu, S. A. Levin and Y. Iwasa, Influence of nonlinear incidence rates upon the behavior of SIRS epidemiological models, J. Math. Biol. 23 (1986), no. 2, 187-204. · Zbl 0582.92023
[23] X. Mao, Stochastic Differential Equations and Their Applications, Horwood, Chichester, 1997. · Zbl 0892.60057
[24] X. Mao, The truncated Euler-Maruyama method for stochastic differential equations, J. Comput. Appl. Math. 290 (2015), 370-384. · Zbl 1330.65016
[25] X. Mao, Convergence rates of the truncated Euler-Maruyama method for stochastic differential equations, J. Comput. Appl. Math. 296 (2016), 362-375. · Zbl 1378.65036
[26] X. Mao, G. Marion and E. Renshaw, Environmental Brownian noise suppresses explosions in population dynamics, Stochastic Process. Appl. 97 (2002), no. 1, 95-110. · Zbl 1058.60046
[27] R. May and R. Anderson, Transmission dynamics of HIV infection, Nature 326 (1987), 137-142.
[28] Z. Qi-Min, L. Wen-An and N. Zan-Kan, Existence, uniqueness and exponential stability for stochastic age-dependent population, Appl. Math. Comput. 154 (2004), no. 1, 183-201. · Zbl 1051.92033
[29] A. Rathinasamy, M. Chinnadurai and S. Athithan, Analysis of exact solution of stochastic sex-structured HIV/AIDS epidemic model with effect of screening of infectives, Math. Comput. Simulation 179 (2021), 213-237. · Zbl 1524.92111
[30] J. Ren, Q. Zhang, X. Li, F. Cao and M. Ye, A stochastic age-structured HIV/AIDS model based on parameters estimation and its numerical calculation, Math. Comput. Simulation 190 (2021), 159-180. · Zbl 1540.92228
[31] S. Sabanis, A note on tamed Euler approximations, Electron. Commun. Probab. 18 (2013), 1-10. · Zbl 1329.60237
[32] S. Sabanis, Euler approximations with varying coefficients: the case of superlinearly growing diffusion coefficients, Ann. Appl. Probab. 26 (2016), no. 4, 2083-2105. · Zbl 1352.60101
[33] H. Tuckwell and E. Corfec, A stochastic model for early HIV-1 population dynamics, J. Theoret. Biol. 195 (1998), no. 4, 451-463.
[34] J. Wang, J. Lang and X. Zou, Analysis of an age structured HIV infection model with virus-to-cell infection and cell-to-cell transmission, Nonlinear Anal. Real World Appl. 34 (2017), 75-96. · Zbl 1352.92174
[35] J. Wang, R. Zhang and T. Kuniya, Global dynamics for a class of age-infection HIV models with nonlinear infection rate, J. Math. Anal. Appl. 432 (2015), no. 1, 289-313. · Zbl 1320.92077
[36] Z. Wang and X. Li, Stability and moment boundedness of the stochastic linear age-structured model, J. Dynam. Differential Equations 31 (2019), no. 4, 2109-2125. · Zbl 1427.37044
[37] J. Yang, X. Wang and X. Li, Global stability of an HIV/AIDS model with stochastic perturbation, Asian-Eur. J. Math. 4 (2011), no. 2, 349-358. · Zbl 1222.34056
[38] Q. Zhang, Exponential stability of numerical solutions to a stochastic age-structured population system with diffusion, J. Comput. Appl. Math. 220 (2008), no. 1-2, 22-33. · Zbl 1149.65009
[39] The Chinese Center for Disease Control and Prevention http://www.chinacdc.cn/.
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