Nonlinear state-dependent pulse control for an SIRS epidemic model with varying size and its application to the transmission of brucellosis
Mathematical modelling of natural phenomena, Tome 16 (2021), article no. 58.

Voir la notice de l'article provenant de la source EDP Sciences

As the disease spreads, it will inevitably cause important damage to the life and health of the population, resulting in changes in the population quantity. In addition, in some economically underdeveloped areas, limited medical resources will also have an important impact on the prevention and control of diseases. Based on these, a susceptible-infected-recovered-susceptible (SIRS) epidemic model is established, where state-dependent pulse control strategy, varying total population and limited medical resources are introduced. By using the qualitative theory of ordinary differential equation, differential inequality techniques, Poincaré map, and other methods, some sufficient conditions of the existence and orbital asymptotical stability of positive order-1 or order-2 periodic solution are obtained in various situations. Theoretical results imply that the proportion of infected class can be controlled at a desired low level for a long time and disease will not break out among population. Finally, based on realistic parameters of brucellosis in ruminants, numerical simulations have been performed to expalin/extend our analytical results and the feasibility of the state-dependent feedback control strategy.
DOI : 10.1051/mmnp/2021050

Lin-Fei Nie 1 ; Fuwei Zhang 1 ; Lin Hu 1

1 College of Mathematics and System Science, Xinjiang University, Urumqi 830017, P.R. China.
@article{MMNP_2021_16_a58,
     author = {Lin-Fei Nie and Fuwei Zhang and Lin Hu},
     title = {Nonlinear state-dependent pulse control for an {SIRS} epidemic model with varying size and its application to the transmission of brucellosis},
     journal = {Mathematical modelling of natural phenomena},
     eid = {58},
     publisher = {mathdoc},
     volume = {16},
     year = {2021},
     doi = {10.1051/mmnp/2021050},
     language = {en},
     url = {https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2021050/}
}
TY  - JOUR
AU  - Lin-Fei Nie
AU  - Fuwei Zhang
AU  - Lin Hu
TI  - Nonlinear state-dependent pulse control for an SIRS epidemic model with varying size and its application to the transmission of brucellosis
JO  - Mathematical modelling of natural phenomena
PY  - 2021
VL  - 16
PB  - mathdoc
UR  - https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2021050/
DO  - 10.1051/mmnp/2021050
LA  - en
ID  - MMNP_2021_16_a58
ER  - 
%0 Journal Article
%A Lin-Fei Nie
%A Fuwei Zhang
%A Lin Hu
%T Nonlinear state-dependent pulse control for an SIRS epidemic model with varying size and its application to the transmission of brucellosis
%J Mathematical modelling of natural phenomena
%D 2021
%V 16
%I mathdoc
%U https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2021050/
%R 10.1051/mmnp/2021050
%G en
%F MMNP_2021_16_a58
Lin-Fei Nie; Fuwei Zhang; Lin Hu. Nonlinear state-dependent pulse control for an SIRS epidemic model with varying size and its application to the transmission of brucellosis. Mathematical modelling of natural phenomena, Tome 16 (2021), article  no. 58. doi : 10.1051/mmnp/2021050. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2021050/

[1] E. Abatih, L. Ron, N. Speybroeck, B. Williams, D. Berkvens Mathematical analysis of the tranmission dynamics of brucellosis among bison Math. Method. Appl. Sci 2015 3818 3832

[2] B.E. Aïnseba, C. Benosman, P. Megal A model for ovine brucellosis incorporating direct and indirect transmission J. Biol. Dyn 2010 2 11

[3] S. Busenberg, P. Van Den Driessche Analysis of a disease transmission model in a population with varying size J. Math. Biol 1990 257 270

[4] S. Busenberg, K. Cooke, H. Thieme Demographic change and persisitence of HIV/AIDS in a heterogengeous population SIAM J. Appl. Math 1991 1030 1052

[5] R.S. Cantrell, C. Cosner, W.F. Faganantrell Brucellosis, botflies, and brainworms: the impact of edge habitats on pathogen transmission and species extinction J. Math. Biol 2001 95 119

[6] A. Dobson, M. Meagher The population dynamics of Brucellosis in the Yellowstone national park Ecology 1996 1026 1036

[7] C. Geremia, P.J. White, J.A. Hoeting, R.L. Wallen, F.G.R. Watson, D. Blanton, N.T. Hobbs Integrating population-and individual-level information in a movement model of Yellowstone bison Ecol. Appl 2014 346 362

[8] J. González-Gunmán, R. Naulin Analysis of a model of bovine brucellosis using singular perturbations J. Math. Biol 1994 211 223

[9] J.K. Hale and H. Kocak, Dynamics and Bifurcations. Texts in Applied Mathematics, Springer-Verlag, New York (1991).

[10] Z.L. He, J.G. Li, L.F. Nie, Z. Zhao Nonlinear state-dependent feedback control strategy in the SIR epidemic model with resource limitation Adv. Differ. Equ.-Ny 2017

[11] Q. Hou, X. Sun, J. Zhang, Y. Liu, Y. Wang, Z. Jin Modeling the transmission dynamics of sheep brucellosis in Inner Mongolia Autonomous Region, China Math. Biosci 2013 51 58

[12] T. Kuniya, Y. Muroya Global stability of a multi-group SIS epidemic model with varying total population size Appl. Math. Comput 2015 785 798

[13] V. Lakshmikantham, D.D. Bainov and P.S. Simeonov, Theory of Impulsive Differential Equations. World Scientific, Singapore (1989).

[14] H. Li, R. Peng, F.B. Wang Varying total population enhances disease persistence: Qualitative analysis on a diffusive SIS epidemic model J. Differ. Equ 2017 885 913

[15] M.Y. Li, J.R. Graef, L. Wang, J. Karsai Global dynamics of a SEIR model with varying total population size Math. Biosci 1999 191 213

[16] M.T. Li, G.Q. Sun, Y.F. Wu, J. Zhang, Z. Jin Transmission dynamics of a multi-group brucellosis model with mixed cross infection in public farm Appl. Math. Comput 2014 582 594

[17] P.O. Lolika, S. Mushayabasa, C.P. Bhunu, C. Modnak, J. Wang Modeling and analyzing the effects of seasonality on brucellosis infection Chaos Soliton. Fract 2017 338 349

[18] G. Lu, Z. Lu Global asymptotic stability for the SEIRS models with varying total population size Math. Biosci 2018 17 25

[19] B. Nannyonga, G.G. Mwanga, L.S. Luboobi An optimal control problem for ovine brucellosis with culling J. Biol. Dyn 2015 198 214

[20] J. Nie, G.Q. Sun, X.D. Sun, J. Zhang, N. Wang, Y.M. Wang, C.J. Shen, B.X. Huang, Z. Jin Modeling the transmission dynamics of dairy cattle brucellosis in Jilin province, China J. Biol. Syst 2014 533 554

[21] L.F. Nie, Z.D. Teng, B.Z. Guo A state dependent pulse control strategy for a SIRS epidemic system B. Math. Biol 2013 1697 1715

[22] L.F. Nie, Z.D. Teng, I.H. Jung Complex dynamic behavior in a viral model with state feedback control strategies Nonlinear Dyn 2014 1223 1236

[23] W. Qin, S. Tang, R.A. Cheke Nonlinear pulse vaccination in an SIR epidemic model with resource limitation Abstr. Appl. Anal 2013 4339 4344

[24] L.E. Samartino Brucellosis in Argentina Vet. Microbiol 2002 71 80

[25] M.M.H. Sewell and D.W. Brocklesby, Handbook of Animal Diseases in the Tropics, 4th edn. Balliere Tindall, Toronto (1990).

[26] P.S. Simeonov, D.D. Bainov Orbital stability of periodic solutions of autonomous systems with impulse effect Int. J. Systems Sci 1988 2561 2585

[27] S. Sun, C. Guo, C. Qin Dynamic behaviors of a modified predator-prey model with state-dependent impulsive effects Adv. Differ. Equ.-Ny 2016

[28] S. Tang, Y. Xiao, D. Clancy New modelling approach concerning integrated disease control and cost-effectivity Nonlinear Anal.-Theory Methods Appl 2005 439 471

[29] J. Touboul, R. Brette Spiking dynamics of bidimensional integrate-and-fire neurons SIAM J. Appl. Dyn. Syst 2009 1462 1506

[30] J.J. Treanor, J.S. Johnson, R.L. Wallen, S. Cilles, P.H. Crowley, J.J. Cox, D.S. Maehr, P.J. White, G.E. Plumb Vaccination strategiesfor managing brucellosis in Yellowstone bison Vaccine 2010 F64 F72

[31] Q. Xiao, B. Dai, B. Xu, L. Bao Homoclinic bifurcation for a general state-dependent Kolmogorov type predator-prey model with harvesting Nonlinear Anal.-Real World Appl 2015 263 273

[32] F. Xie, R.D. Horan Disease and behavioral dynamics for brucellosis control in EIk and Cattle in the greater Yellowstone area J. Agric. Resour. Econ 2009 11 33

[33] C. Yang, P.O. Lolika, S. Mushayabasa, J. Wang Modeling the spatiotemporal variations in brucellosis transmission Nonlinear Anal.-Real World Appl 2017 49 67

[34] W. Zhao, J. Li, X. Meng Dynamical analysis of SIR epidemic model with nonlinear pulse vaccination and lifelong immunity Discrete Dyn. Nat. Soc 2015

[35] T. Zhang, J. Zhang, X. Meng, T. Zhang Geometric analysis of a pest management model with Holling’s type III functional response and nonlinear state feedback control Nonlinear Dyn 2016 1529 1539

Cité par Sources :