Spatio-temporal dynamics of an age-structured reaction-diffusion system of epidemic type subjected by Neumann boundary condition
Cong-Bang Trang, Hoang-Hung Vo

TL;DR
This paper investigates the complex spatio-temporal behavior of an age-structured epidemic reaction-diffusion model with Neumann boundary conditions, establishing existence, stability, and the basic reproduction number using advanced mathematical techniques.
Contribution
It introduces a novel analysis of an age-structured SIS epidemic model with reaction-diffusion dynamics, addressing challenges posed by non-separable mortality rates and lack of comparison principle.
Findings
Existence of time-dependent solutions established.
Disease-free equilibrium stability characterized by R0<1.
Analysis highlights limitations of existing semi-flow methods.
Abstract
This paper is concerned with the spatio-temporal dynamics of an age-structured reaction-diffusion system of KPP-epidemic type (SIS), subject to Neumann boundary conditions and incorporating blow-up type death rate. We first establish the existence of time dependent solutions using age-structured semigroup theory. Afterward, the basic reproduction number is derived by linearizing the system around the disease-free equilibrium state. In the case , the existence, uniqueness and stability of disease-free equilibrium are shown by using -limit set approach of Langlais \cite{langlais_large_1988}, combined with the technique developed in recent works of Zhao et al. \cite{zhao_spatiotemporal_2023} and Ducrot et al. \cite{ducrot_age-structured_2024}. We highlight that the absence of a general comparison principle for the age-structured SIS-model and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMathematical and Theoretical Epidemiology and Ecology Models · COVID-19 epidemiological studies · Mathematical Biology Tumor Growth
