Abstract
In this paper, we perform qualitative analysis to two SIS epidemic models in a patchy environment, without and with linear recruitment. The model without linear recruitment was proposed and studied by Allen et al. (SIAM J Appl Math 67(5):1283–1309, 2007). This model possesses a conserved total population number, whereas the model with linear recruitment has a varying total population. However, both models have the same basic reproduction number. For both models, we establish the global stability of endemic equilibrium in a special case, which partially solves an open problem. Then we investigate the asymptotic behavior of endemic equilibrium as the mobility of infected and/or susceptible population tends to zero. Though the basic reproduction number is a well-known critical index, our theoretical results strongly suggest that other factors such as the variation of total population number and individual movement may also play vital roles in disease prediction and control. In particular, our results imply that the variation of total population number can cause infectious disease to become more threatening and difficult to control.
Similar content being viewed by others
References
Allen LJS, Bolker BM, Lou Y, Nevai AL (2007) Asymptotic profiles of the steady states for an SIS epidemic patch model. SIAM J Appl Math 67(5):1283–1309
Allen LJS, Bolker BM, Lou Y, Nevai AL (2008) Asymptotic profiles of the steady states for an SIS epidemic reaction–diffusion model. Discrete Contin Dyn Syst 21:1–20
Allen LJS, Bolker BM, Lou Y, Nevai AL (2009) Spatial patterns in a discrete-time SIS patch model. J Math Biol 58:339–375
Anderson RM, May RM (1979) Population biology of infectious diseases. Nature 280:361–367
Anderson RM, May RM (1991) Infectious diseases of humans: dynamics and control. Oxford University Press, Oxford
Arino J (2009) Diseases in metapopulations. In: Ma Z, Zhou Y, Wu J (eds) Modeling and Dynamics of Infectious Diseases, Ser. Contemp. Appl. Math., vol 11. World Scientific, Singapore, pp 65–123
Bapat RB, Raghavan TES (1997) Nonnegative matrices and applications. Cambridge University Press, Cambridge
Berman A, Plemmons RJ (1979) Nonnegative matrices in the mathematical sciences. Academic Press, New York
Bichara D, Iggidr A (2018) Multi-patch and multi-group epidemic models: a new framework. J Math Biol 77:107–134
Brauer F, Castillo-Chavez C (2000) Mathematical models in population biology and epidemiology. Springer, New York
Brauer F, Nohel JA (1989) The qualitative theory of ordinary differential equations. Dover, New York
Clancy D (1996) Carrier-borne epidemic models incorporating population mobility. Math Biosci 132:185–204
Cooke K, van den Driessche P, Zou X (1999) Interaction of maturation delay and nonlinear birth in population and epidemic models. J Math Biol 39:332–352
Cosner C, Beier JC, Cantrell RS, Impoinvil D, Kapitanski L, Potts MD, Troyoe A, Ruan S (2009) The effects of human movement on the persistence of vector-borne diseases. J Theor Biol 258:550–560
Cui R, Lou Y (2016) A spatial SIS model in advective heterogeneous environments. J Differ Equ 261:3305–3343
Cui J-A, Tao X, Zhu H (2008) An SIS infection model incorporating media coverage. Rocky Mt J Math 38:1323–1334
Cui R, Lam K-Y, Lou Y (2017) Dynamics and asymptotic profiles of steady states of an epidemic model in advective environments. J Differ Equ 263:2343–2373
Deng K, Wu Y (2016) Dynamics of a susceptible–infected–susceptible epidemic reaction–diffusion model. Proc R Soc Edinb Sect A 146:929–946
Dhirasakdanon T, Thieme HR, van den Driessche P (2007) A sharp threshold for disease persistence in host metapopulations. J Biol Dyn 1:363–378
Diekmann O, Heesterbeek JAP (2000) Mathematical epidemiology of infectious diseases. Wiley, Chichester
Diekmann O, Heesterbeek JAP, Metz JAJ (1990) On the definition and the computation of the basic reproduction ratio \({\cal{R}}_0\) in models for infectious diseases in heterogeneous populations. J Math Biol 28:365–382
Ding W, Huang W, Kansakar S (2013) Traveling wave solutions for a diffusive SIS epidemic model. Discrete Contin Dyn Syst Ser B 18:1291–1304
Faddy MJ (1986) A note on the behavior of deterministic spatial epidemics. Math Biosci 80:19–22
Gao D, Ruan S (2011) An SIS patch model with variable transmission coefficients. Math Biosci 232:110–115
Ge J, Kim KI, Lin Z, Zhu H (2015) A SIS reaction–diffusion–advection model in a low-risk and high-risk domain. J Differ Equ 259:5486–5509
Ge J, Lin L, Zhang L (2017) A diffusive SIS epidemic model incorporating the media coverage impact in the heterogeneous environment. Discrete Contin Dyn Syst Ser B 22:2763–2776
Hale J (1969) Ordinary differential equations. Wiley, New York
Hethcote HW (1976) Qualitative analyses of communicable disease models. Math Biosci 28:335–356
Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42:599–653
Hirsch WM, Smith HL, Zhao X-Q (2001) Chain transitivity, attractivity, and strong repellors for semidynamical systems. J Dyn Differ Equ 13:107–131
Huang W, Han M, Liu K (2010) Dynamics of an SIS reaction–diffusion epidemic model for disease transmission. Math Biosci Eng 7:51–66
Kermack WO, McKendrick AG (1927) Contributions to the mathematical theory of epidemics-I. Proc R Soc Lond Ser A 115:700–721
Kousuke K, Matsuzawa H, Peng R (2017) Concentration profile of endemic equilibrium of a reaction–diffusion–advection SIS epidemic model. Calc Var Partial Differ Equ 56:112
Lancaster P, Tismenetsky M (1985) The theory of matrices, 2nd edn. Academic Press, Orlando
LaSalle J (1960) Some extensions of Lyapunov’s second method. IRE Trans Circuit Theory 7:520–527
Li MY, Shuai Z (2009) Global stability of an epidemic model in a patchy environment. Can Appl Math Q 17:175–187
Li MY, Shuai Z (2010) Global-stability problem for coupled systems of differential equations on networks. J Differ Equ 248:1–20
Li J, Zou X (2009) Generalization of the Kermack–McKendrick SIR model to a patchy environment for a disease with latency. Math Model Nat Phenom 4:92–118
Li J, Zou X (2010) Dynamics of an epidemic model with non-local infections for diseases with latency over a patchy environment. J Math Biol 60:645–686
Li B, Li H, Tong Y (2017a) Analysis on a diffusive SIS epidemic model with logistic source. Z Angew Math Phys 68:96
Li H, Peng R, Wang F-B (2017b) Varying total population enhances disease persistence: qualitative analysis on a diffusive SIS epidemic model. J Differ Equ 262:885–913
Li H, Peng R, Xiang T (2017c) Dynamics and asymptotic profiles of endemic equilibrium for two frequency-dependent SIS epidemic models with cross-diffusion. Eur J Appl Math. https://doi.org/10.1017/S0956792518000463
Li H, Peng R, Wang Z-A (2018) On a diffusive susceptible–infected–susceptible epidemic model with mass action mechanism and birth-death effect: analysis, simulations, and comparison with other mechanisms. SIAM J Appl Math 78:2129–2153
Martcheva M (2015) An introduction to mathematical epidemiology. Springer, New York
Ortega JM (1987) Matrix theory: a second course. Plenum Press, New York
Peng R (2009) Asymptotic profiles of the positive steady state for an SIS epidemic reaction–diffusion model. Part I. J Differ Equ 247:1096–1119
Peng R, Liu S (2009) Global stability of the steady states of an SIS epidemic reaction–diffusion model. Nonlinear Anal 71:239–247
Peng R, Yi F (2013) Asymptotic profile of the positive steady state for an SIS epidemic reaction–diffusion model: effects of epidemic risk and population movement. Phys D 259:8–25
Peng R, Zhao X (2012) A reaction–diffusion SIS epidemic model in a time-periodic environment. Nonlinearity 25:1451–1471
Salmani M, van den Driessche P (2006) A model for disease transmission in a patchy environment. Discrete Contin Dyn Syst Ser B 6:185–202
Smith H (1995) Monotone dynamical systems: an introduction to the theory of competitive and cooperative systems, mathematical surveys and monographs. AMS, Providence
Smith H, Waltman P (1995) The theory of the chemostat. Dynamics of microbial competition. Cambridge University Press, Cambridge
Sun C, Wei Y, Arino J, Khan K (2011) Effect of media-induced social distancing on disease transmission in a two patch setting. Math Biosci 230:87–95
Thieme HR (1993) Persistence under relaxed point-dissipativity (with application to an endemic model). SIAM J Math Anal 24:407–435
van den Driessche P, Watmough J (2002) Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math Biosci 180:29–48
Wang W (2007) Epidemic models with population dispersal. In: Takeuchi Y, Iwasa Y, Sato K (eds) Mathematics for life sciences and medicine. Springer, Berlin, pp 67–95
Wang W, Mulone G (2003) Threshold of disease transmission in a patch environment. J Math Anal Appl 285:321–335
Wang W, Zhao X-Q (2004) An epidemic model in a patchy environment. Math Biosci 190:97–112
Wang W, Zhao X-Q (2005) An age-structured epidemic model in a patchy environment. SIAM J Appl Math 65:1597–1614
Wen X, Ji J, Li B (2018) Asymptotic profiles of the endemic equilibrium to a diffusive SIS epidemic model with mass action infection mechanism. J Math Anal Appl 458:715–729
Wu Y, Zou X (2016) Asymptotic profiles of steady states for a diffusive SIS epidemic model with mass action infection mechanism. J Differ Equ 261:4424–4447
Acknowledgements
H. Li was partially supported by NSF of China (Nos. 11701180, 11671143 and 11671144), and R. Peng was partially supported by NSF of China (Nos. 11671175 and 11571200), the Priority Academic Program Development of Jiangsu Higher Education Institutions, Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (No. PPZY2015A013) and Qing Lan Project of Jiangsu Province. The authors would like to express their sincere gratitude to two anonymous reviewers for bringing references (Li and Shuai 2010; Salmani and van den Driessche 2006; Sun et al. 2011) to their attention, and for valuable comments and suggestions which significantly improve the exposition of this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Here we shall discuss the global dynamics of the ODE problem (6.2) with initial data \(S(0) = S_0 \ge 0\) and \(I(0) = I_0 >0\). Our main result reads as follows.
Theorem A
The following statement holds.
-
(i)
If \(\beta \le \theta \gamma \), then \(\lim _{t\rightarrow \infty }(S(t),I(t)) = (S^*,0)\), where \(S^*\) is some positive constant;
-
(ii)
If \(\beta >\theta \gamma \), then \(\lim _{t\rightarrow \infty }(S(t),I(t)) = (0,0).\)
Proof
Clearly, (S, I) exists globally, and \(S(t)>0\), \(I(t)>0\) for all \(t>0\). One also observes that
As a result, \((S+I)(t)\) is strictly decreasing and the limit \(\lim _{t\rightarrow \infty }(S+I)(t)\) exists. Obviously, \(S'(t)\) and \(I'(t)\) remain bounded for \(t \in (0,\infty )\). In view of Peng and Zhao (2012, Lemma 4.1) and (B3), we must have
Thus, \(\lim _{t\rightarrow \infty }S(t)\) exists and we denote it by \(S^*\).
-
(i)
By virtue of \(\beta \le \theta \gamma \), from the S-equation it follows that
$$\begin{aligned} \frac{dS}{dt}\ge (\theta \gamma - \beta )I\ge 0,\quad \forall t>0. \end{aligned}$$That is, S(t) is nondecreasing and hence \(S^* = \lim _{t\rightarrow \infty } S(t) >0.\)
-
(ii)
We first consider the case of \(\beta >\gamma \). Then there exists some \(\epsilon _0>0\) such that \(\beta - \gamma - \epsilon _0>0\). Now suppose \(S^*>0\). Since \(\frac{\beta S}{S+I} \rightarrow \beta \) as \(t\rightarrow \infty \) by (B4), there exists some \(T>0\) fulfilling \(\frac{\beta S}{S+I} > \beta - \epsilon _0\) for all \(t>T\). Notice that
$$\begin{aligned} \frac{d I}{dt} = I\left( \frac{\beta S}{S+I} - \gamma \right)> (\beta - \gamma - \epsilon _0)I>0,\quad \forall t>T, \end{aligned}$$from which it readily follows that \(I(t) \rightarrow \infty \) as \(t\rightarrow \infty \), contradicting (B4). Therefore, \(S^* = 0\) in this case.
We now consider the case of \(\beta = \gamma \). Let \(U(t)= S(t)/I(t)\). Then direct calculations show that \(\frac{d U}{dt} = \beta \theta \). Consequently, it holds
$$\begin{aligned} U(t) = \beta \theta t + \frac{S_0}{I_0} \quad \text{ and } \text{ so }\quad S(t) = \left( \beta \theta t+ \frac{S_0}{I_0} \right) I(t),\quad \forall t \ge 0. \end{aligned}$$Then using the I-equation, we have
$$\begin{aligned} \frac{dI}{dt} = \beta I \left( \frac{S}{S+I} - 1\right) = \beta I \left( \frac{\beta \theta t + S_0/{I_0}}{\beta \theta t+S_0/{I_0} +1 } -1 \right) = -\frac{\beta I}{\beta \theta t + S_0/{I_0} + 1},\quad \forall t>0. \end{aligned}$$Solving this ODE, we obtain
$$\begin{aligned} I(t) = I_0 \left( \frac{S_0/{I_0} + 1}{\beta \theta t + S_0/{I_0} + 1} \right) ^{1/\theta },\quad \forall t\ge 0, \end{aligned}$$and in turn,
$$\begin{aligned} S(t) =I_0 \left( \beta \theta t+ \frac{S_0}{I_0} \right) \left( \frac{S_0/{I_0} + 1}{\beta \theta t + S_0/{I_0} + 1} \right) ^{1/\theta },\quad \forall t\ge 0. \end{aligned}$$Thus, \(S^* = \lim _{t\rightarrow \infty }S(t) = 0.\)
It remains to handle the case of \(\theta \gamma<\beta < \gamma \). We pick \(\delta _0>0\) such that \(\theta \gamma - \beta +\delta _0<0\). We again suppose that \(S^*>0\). Then there exists some \(T_1>0\) satisfying
$$\begin{aligned} \beta - \delta _0< \beta \frac{S(t)}{S(t)+I(t)} < \beta +\delta _0,\quad \forall t>T_1. \end{aligned}$$It follows from the I-equation that
$$\begin{aligned} \frac{dI}{dt}= I \left( \beta \frac{S}{S+I} -\gamma \right)> I(\beta -\gamma - \delta _0), \quad \forall t>T_1. \end{aligned}$$Then
$$\begin{aligned} I(t) > I(T_1) e^{-(\gamma - \beta +\delta _0)(t-T_1)},\quad \forall t\ge T_1. \end{aligned}$$Making use of the S-equation, we deduce that
$$\begin{aligned} \frac{dS}{dt} = I \left( \theta \gamma - \beta \frac{S}{S+I} \right)< I(\theta \gamma - \beta +\delta _0) < I(T_1) (\theta \gamma - \beta +\delta _0) e^{-(\gamma - \beta +\delta _0)(t-T_1)},\quad \forall t>T_1. \end{aligned}$$Since \(S(t) \rightarrow S^*>0\) as \(t\rightarrow \infty \), then for any \(\epsilon \) satisfying
$$\begin{aligned} 0< \epsilon < -I(T_1) \frac{\theta \gamma - \beta +\delta _0}{\gamma - \beta +\delta _0}, \end{aligned}$$(B5)there exists some \(T_2>0\) such that \(S(t)<S^*+\epsilon \) for all \(t\ge T_2.\) We now take \(T=\max \{T_1,T_2\}\). Consider the following auxiliary problem
$$\begin{aligned} \left\{ \begin{array}{ll} \displaystyle \frac{d u}{dt} = I(T_1) (\theta \gamma - \beta +\delta _0) e^{-(\gamma - \beta +\delta _0)(t-T_1)},\quad t>T,\\ \displaystyle u(T)=S^*+\epsilon , \end{array}\right. \end{aligned}$$(B6)It can be easily checked that the unique solution u(t) of (B6) satisfies
$$\begin{aligned} \lim _{t\rightarrow \infty } u(t) = S^*+\epsilon + I(T_1) \frac{\theta \gamma -\beta +\delta _0}{\gamma - \beta +\delta _0}<S^*, \end{aligned}$$(B7)thanks to (B5). On the other hand, a simple comparison of S and u indicates that \(S(t) < u(t)\) for all \(t>T\), contradicting (B7) if we send \(t\rightarrow \infty \). Thus, we must have \(S^*=0.\)
\(\square \)
Rights and permissions
About this article
Cite this article
Li, H., Peng, R. Dynamics and asymptotic profiles of endemic equilibrium for SIS epidemic patch models. J. Math. Biol. 79, 1279–1317 (2019). https://doi.org/10.1007/s00285-019-01395-8
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00285-019-01395-8
Keywords
- SIS epidemic patch model
- Disease-free equilibrium
- Endemic equilibrium
- Basic reproduction number
- Global dynamics
- Asymptotic profile