# American Institute of Mathematical Sciences

December  2018, 23(10): 4223-4242. doi: 10.3934/dcdsb.2018134

## Global stability of a diffusive and delayed HBV infection model with HBV DNA-containing capsids and general incidence rate

 1 Department of Mathematics, Yunnan Normal University, Kunming 650092, China 1 School of Science, Nanjing University of Science and Technology, Nanjing 210094, China

* Corresponding author: Haihong Liu

Received  September 2017 Revised  December 2017 Published  April 2018

The aim of this paper is to study the dynamics of a new chronic HBV infection model that includes spatial diffusion, three time delays and a general incidence function. First, we analyze the well-posedness of the initial value problem of the model in the bounded domain. Then, we define a threshold parameter $R_{0}$ called the basic reproduction number and show that our model admits two possible equilibria, namely the infection-free equilibrium $E_{1}$ as well as the chronic infection equilibrium $E_{2}$. Further, by constructing two appropriate Lyapunov functionals, we prove that $E_{1}$ is globally asymptotically stable when $R_{0}<1$, corresponding to the viruses are cleared and the disease dies out; if $R_{0}>1$, then $E_{1}$ becomes unstable and the equilibrium point $E_{2}$ appears and is globally asymptotically stable, which means that the viruses persist in the host and the infection becomes chronic. An application is provided to confirm the main theoretical results. Additionally, it is worth saying that, our results suggest theoretically useful method to control HBV infection and these results can be applied to a variety of possible incidence functions presented in a series of other papers.

Citation: Ting Guo, Haihong Liu, Chenglin Xu, Fang Yan. Global stability of a diffusive and delayed HBV infection model with HBV DNA-containing capsids and general incidence rate. Discrete & Continuous Dynamical Systems - B, 2018, 23 (10) : 4223-4242. doi: 10.3934/dcdsb.2018134
##### References:

show all references

##### References:
Schematic view of the replication process of HBV
Diagrammatic representation of the mathematical model for HBV infection
The numerical approximations of system (21)-(23) with parameters $s = 2.6\times10^{7}$, $\mu = 0.01$, $\delta = 0.053$, $a = 150$, $\beta = 0.87$, $c = 3.8$, $d_{v} = 0.01$, $b_{1} = b_{2} = 0.01$, $\alpha_{1} = 0.2$, $\alpha_{2} = 0.28$, $\alpha_{3} = 0.1$, $\tau_{1} = 10$, $\tau_{2} = 0$, $\tau_{3} = 0$ and $k = 3\times10^{-5}$, showing that solution trajectories converge to the infection-free equilibrium $E_{1}: (H_{1}, I_{1}, D_{1}, V_{1}) = (2.6\times10^{9}, 0, 0, 0)$
The numerical approximations of system (21)-(23) with parameters $s = 2.6\times10^{7}$, $\mu = 0.01$, $\delta = 0.053$, $a = 150$, $\beta = 0.87$, $c = 3.8$, $d_{v} = 0.01$, $b_{1} = b_{2} = 0.01$, $\alpha_{1} = 0.2$, $\alpha_{2} = 0.28$, $\alpha_{3} = 0.1$, $\tau_{1} = 10$, $\tau_{2} = 0$, $\tau_{3} = 0$ and $k = 1.67\times10^{-4}$, showing that solution trajectories converge to the chronic infection equilibrium $E_{2}: (H_{2}, I_{2}, D_{2}, V_{2}) = (1.61\times10^{9}, 2.53\times10^{7}, 4.12\times10^{9}, 9.43\times10^{8})$
The graphs of the basic reproduction number $R_{0}$ in terms of some parameters: (a) $R_{0}$ in terms of $\alpha_{1}$ and $\alpha_{2}$, (b) $R_{0}$ as a function of $\alpha_{1}$ and $\alpha_{3}$, and (c) $R_{0}$ in terms of $\alpha_{2}$ and $\alpha_{3}$. Here, $s = 2.6\times10^{7}$, $\mu = 0.01$, $\delta = 0.053$, $a = 150$, $\beta = 0.87$, $c = 3.8$, $b_{1} = 0.01$, $\tau_{1} = 5.8$, $\tau_{2} = 6$, $\tau_{3} = 4$ and $k = 2.4\times10^{-3}$
The plots of the basic reproduction number $R_{0}$ as a function of three delays $\tau_{1}$, $\tau_{2}$ and $\tau_{3}$. Here, $s = 2.6\times10^{7}$, $\mu = 0.01$, $\delta = 0.053$, $a = 150$, $\beta = 0.87$, $c = 3.8$, $b_{1} = 0.01$, $\alpha_{1} = 0.2$, $\alpha_{2} = 0.28$, $\alpha_{3} = 0.1$ and $k = 2.4\times10^{-3}$. (a) $\tau_{3} = 4$, (b) $\tau_{2} = 6$, and (c) $\tau_{1} = 5.8$
 [1] Yu Ji. Global stability of a multiple delayed viral infection model with general incidence rate and an application to HIV infection. Mathematical Biosciences & Engineering, 2015, 12 (3) : 525-536. doi: 10.3934/mbe.2015.12.525 [2] Yu Yang, Yueping Dong, Yasuhiro Takeuchi. Global dynamics of a latent HIV infection model with general incidence function and multiple delays. Discrete & Continuous Dynamical Systems - B, 2019, 24 (2) : 783-800. doi: 10.3934/dcdsb.2018207 [3] Yu Ji, Lan Liu. Global stability of a delayed viral infection model with nonlinear immune response and general incidence rate. Discrete & Continuous Dynamical Systems - B, 2016, 21 (1) : 133-149. doi: 10.3934/dcdsb.2016.21.133 [4] Hui Miao, Zhidong Teng, Chengjun Kang. Stability and Hopf bifurcation of an HIV infection model with saturation incidence and two delays. Discrete & Continuous Dynamical Systems - B, 2017, 22 (6) : 2365-2387. doi: 10.3934/dcdsb.2017121 [5] Hong Yang, Junjie Wei. Global behaviour of a delayed viral kinetic model with general incidence rate. Discrete & Continuous Dynamical Systems - B, 2015, 20 (5) : 1573-1582. doi: 10.3934/dcdsb.2015.20.1573 [6] Shouying Huang, Jifa Jiang. Epidemic dynamics on complex networks with general infection rate and immune strategies. Discrete & Continuous Dynamical Systems - B, 2018, 23 (6) : 2071-2090. doi: 10.3934/dcdsb.2018226 [7] Shouying Huang, Jifa Jiang. Global stability of a network-based SIS epidemic model with a general nonlinear incidence rate. Mathematical Biosciences & Engineering, 2016, 13 (4) : 723-739. doi: 10.3934/mbe.2016016 [8] Jinling Zhou, Yu Yang. Traveling waves for a nonlocal dispersal SIR model with general nonlinear incidence rate and spatio-temporal delay. Discrete & Continuous Dynamical Systems - B, 2017, 22 (4) : 1719-1741. doi: 10.3934/dcdsb.2017082 [9] Zhaohui Yuan, Xingfu Zou. Global threshold dynamics in an HIV virus model with nonlinear infection rate and distributed invasion and production delays. Mathematical Biosciences & Engineering, 2013, 10 (2) : 483-498. doi: 10.3934/mbe.2013.10.483 [10] Xichao Duan, Sanling Yuan, Kaifa Wang. Dynamics of a diffusive age-structured HBV model with saturating incidence. Mathematical Biosciences & Engineering, 2016, 13 (5) : 935-968. doi: 10.3934/mbe.2016024 [11] Yoji Otani, Tsuyoshi Kajiwara, Toru Sasaki. Lyapunov functionals for multistrain models with infinite delay. Discrete & Continuous Dynamical Systems - B, 2017, 22 (2) : 507-536. doi: 10.3934/dcdsb.2017025 [12] Zhixing Hu, Ping Bi, Wanbiao Ma, Shigui Ruan. Bifurcations of an SIRS epidemic model with nonlinear incidence rate. Discrete & Continuous Dynamical Systems - B, 2011, 15 (1) : 93-112. doi: 10.3934/dcdsb.2011.15.93 [13] Pierre Gabriel. Global stability for the prion equation with general incidence. Mathematical Biosciences & Engineering, 2015, 12 (4) : 789-801. doi: 10.3934/mbe.2015.12.789 [14] Yoji Otani, Tsuyoshi Kajiwara, Toru Sasaki. Lyapunov functionals for virus-immune models with infinite delay. Discrete & Continuous Dynamical Systems - B, 2015, 20 (9) : 3093-3114. doi: 10.3934/dcdsb.2015.20.3093 [15] Hui Cao, Yicang Zhou, Zhien Ma. Bifurcation analysis of a discrete SIS model with bilinear incidence depending on new infection. Mathematical Biosciences & Engineering, 2013, 10 (5&6) : 1399-1417. doi: 10.3934/mbe.2013.10.1399 [16] Andrei Korobeinikov, Philip K. Maini. A Lyapunov function and global properties for SIR and SEIR epidemiological models with nonlinear incidence. Mathematical Biosciences & Engineering, 2004, 1 (1) : 57-60. doi: 10.3934/mbe.2004.1.57 [17] Thaís Jordão, Xingping Sun. General types of spherical mean operators and $K$-functionals of fractional orders. Communications on Pure & Applied Analysis, 2015, 14 (3) : 743-757. doi: 10.3934/cpaa.2015.14.743 [18] Yu Yang, Dongmei Xiao. Influence of latent period and nonlinear incidence rate on the dynamics of SIRS epidemiological models. Discrete & Continuous Dynamical Systems - B, 2010, 13 (1) : 195-211. doi: 10.3934/dcdsb.2010.13.195 [19] Yoichi Enatsu, Yukihiko Nakata. Stability and bifurcation analysis of epidemic models with saturated incidence rates: An application to a nonmonotone incidence rate. Mathematical Biosciences & Engineering, 2014, 11 (4) : 785-805. doi: 10.3934/mbe.2014.11.785 [20] C. Connell McCluskey. Global stability of an $SIR$ epidemic model with delay and general nonlinear incidence. Mathematical Biosciences & Engineering, 2010, 7 (4) : 837-850. doi: 10.3934/mbe.2010.7.837

2018 Impact Factor: 1.008