Towards Understanding the Endemic Behavior of a Competitive Tri-Virus SIS Networked Model

This paper studies the endemic behavior of a multi-competitive networked susceptible-infected-susceptible (SIS) model. Specifically, the paper deals with three competing virus systems (i.e., tri-virus systems). First, we show that a tri-virus system, unlike a bi-virus system, is not a monotone dynamical system. Using the Parametric Transversality Theorem, we show that, generically, a tri-virus system has a finite number of equilibria and that the Jacobian matrices associated with each equilibrium are nonsingular. The endemic equilibria of this system can be classified as follows: a) single-virus endemic equilibria (also referred to as the boundary equilibria), where precisely one of the three viruses is alive; b) 2-coexistence equilibria, where exactly two of the three viruses are alive; and c) 3-coexistence equilibria, where all three viruses survive in the network. We provide a necessary and sufficient condition that guarantees local exponential convergence to a boundary equilibrium. Further, we secure conditions for the nonexistence of 3-coexistence equilibria (resp. for various forms of 2-coexistence equilibria). We also identify sufficient conditions for the existence of a 2-coexistence (resp. 3-coexistence) equilibrium. We identify conditions on the model parameters that give rise to a continuum of coexistence equilibria. More specifically, we establish i) a scenario that admits the existence and local exponential attractivity of a line of coexistence equilibria; and ii) scenarios that admit the existence of, and, in the case of one such scenario, global convergence to, a plane of 3-coexistence equilibria..

Medienart:

Preprint

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

arXiv.org - (2023) vom: 29. März Zur Gesamtaufnahme - year:2023

Sprache:

Englisch

Beteiligte Personen:

Gracy, Sebin [VerfasserIn]
Ye, Mengbin [VerfasserIn]
Anderson, Brian D. O. [VerfasserIn]
Uribe, Cesar A. [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

000
620
Computer Science - Systems and Control
Electrical Engineering and Systems Science - Systems and Control

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XAR039107264