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June  2020, 19(6): 3323-3340. doi: 10.3934/cpaa.2020147

## Asymptotic profiles of steady states for a diffusive SIS epidemic model with spontaneous infection and a logistic source

 School of Mathematical Science, Heilongjiang University, Harbin 150080, China

*Corresponding author

Received  September 2019 Revised  November 2019 Published  March 2020

Fund Project: J. Wang was supported by National Natural Science Foundation of China (No. 11871179), Natural Science Foundation of Heilongjiang Province (No. LC2018002) and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, China. S. Zhu was supported by Graduate Students Innovation Research Program of Heilongjiang University (No. YJSCX2019-206HLJU)

Spatial heterogeneity and movement of population play an important role in disease spread and control in reality. This paper concerns with a spatial Susceptible-Infected-Susceptible epidemic model with spontaneous infection and logistic source, aiming to investigate the asymptotic profiles of the endemic steady state (whenever it exists) for large and small diffusion rates. We firstly establish uniform upper bound of solutions. By studying the local and global stability of the unique constant endemic equilibrium when spatial environment is homogeneous, we apply the well-known Leray-Schuauder degree index formula to confirm the existence of endemic steady state. Our theoretical results suggest that spontaneous infection and varying total population strongly enhance the persistence of disease spread in the sense that disease component of the endemic steady state will not approach zero whenever the large and small diffusion rates of the susceptible or infected population is used. This gives new insights and aspects for infectious disease modeling and control.

Citation: Siyao Zhu, Jinliang Wang. Asymptotic profiles of steady states for a diffusive SIS epidemic model with spontaneous infection and a logistic source. Communications on Pure & Applied Analysis, 2020, 19 (6) : 3323-3340. doi: 10.3934/cpaa.2020147
##### References:
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show all references

##### References:
 [1] L. J. S. Allen, B. M. Bolker, Y. Lou and A. L. Nevai, Asymptotic profiles of the steady states for an SIS epidemic reaction-diffusion model, Discrete Contin. Dyn. Syst., 21 (2008), 1-20.  doi: 10.3934/dcds.2008.21.1.  Google Scholar [2] R. Cui and Y. Lou, A spatial SIS model in advective heterogeneous environments, J. Differ. Equ., 261 (2016), 3305-3343.  doi: 10.1016/j.jde.2016.05.025.  Google Scholar [3] R. Cui, K. Lam and Y. Lou, Dynamics and asymptotic profiles of steady states of an epidemic model in advective environments, J. Differ. Equ., 263 (2017), 2343-2373.  doi: 10.1016/j.jde.2017.03.045.  Google Scholar [4] Z. Du and R. Peng, A priori $L^\infty$ estimates for solutions of a class of reaction-diffusion systems, J. Math. Biol., 72 (2016), 1429-1439.  doi: 10.1007/s00285-015-0914-z.  Google Scholar [5] J. Ge, K. Kim, Z. Lin and H. Zhu, A SIS reaction-diffusion-advection model in a low-risk and high-risk domain, J. Differ. Equ., 259 (2015), 5486-5509.  doi: 10.1016/j.jde.2015.06.035.  Google Scholar [6] P. Hess, Periodic-Parabolic Boundary Value Problems and Positivity, vol. 247, Longman Scientific & Technical Harlow, 1991.  Google Scholar [7] A. Hill, D. Rand, M. Nowak and N. Christakis, Infectious Disease Modeling of Social Contagion in Networks, PLoS Comput. Biol., 6 (2010), e1000968. doi: 10.1371/journal.pcbi.1000968.  Google Scholar [8] D. Le, Dissipativity and global attractors for a class of quasilinear parabolic systems, Commun. Partial Differ. Equ., 22 (1997), 413-433.  doi: 10.1080/03605309708821269.  Google Scholar [9] H. Li, R. Peng and F-B. Wang, Varying total population enhances disease persistence: Qualitative analysis on a diffusive SIS epidemic model, J. Differ. Equ., 262 (2017), 885-913.  doi: 10.1016/j.jde.2016.09.044.  Google Scholar [10] B. Li, H. Li and T. Tong, Analysis on a diffusive SIS epidemic model with logistic source, Z. Angew. Math. Phys., 68 (2017), 96. doi: 10.1007/s00033-017-0845-1.  Google Scholar [11] Y. Lou and W. M. Ni, Diffusion, self-diffusion and cross-diffusion, J. Differ. Equ., 131 (1996), 400-426.  doi: 10.1006/jdeq.1996.0157.  Google Scholar [12] L. Nirenberg, Topic in Nonlinear Functional Nnalysis, American Mathematical Society, Providence, RI, 1974. doi: 10.1090/cln/006.  Google Scholar [13] R. Peng and S. Liu, Global stability of the steady states of an SIS epidemic reaction-diffusion model, Nonlinear Anal. Theory Meth. Appl., 71 (2009), 239-247.  doi: 10.1016/j.na.2008.10.043.  Google Scholar [14] R. Peng, Asymptotic profiles of the positive steady state for an SIS epidemic reaction-diffusion model. Part I, J. Differ. Equ., 247 (2009), 1096-1119.  doi: 10.1016/j.jde.2009.05.002.  Google Scholar [15] R. Peng and F. Yi, Asymptotic profile of the positive steady state for an SIS epidemic reaction-diffusion model: Effects of epidemic risk and population movement, Physica D, 259 (2013), 8-25.  doi: 10.1016/j.physd.2013.05.006.  Google Scholar [16] Y. Tong and C. Lei, An SIS epidemic reaction-diffusion model with spontaneous infection in a spatially heterogeneous environment, Nonlinear Anal. Real World Appl., 41 (2018), 443-460.  doi: 10.1016/j.nonrwa.2017.11.002.  Google Scholar [17] Y. Wu and X. Zou, Asymptotic profiles of steady states for a diffusive SIS epidemic model with mass action infection mechanism, J. Differ. Equ., 261 (2016), 4424-4447.  doi: 10.1016/j.jde.2016.06.028.  Google Scholar [18] S. Zhu and J. Wang, Analysis of a diffusive SIS epidemic model with spontaneous infection and a linear source in spatially heterogeneous environment, Discrete Contin. Dyn. Syst. Ser. B, 25 (2020), 1999-2019.   Google Scholar
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