2021 article
Competing Epidemics on Graphs - Global Convergence and Coexistence
IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2021).
TL;DR:
This paper obtains convergence results for the entirety of the model parameter space, giving precise conditions under which the system globally converges to a trichotomy of possible outcomes: a virus-free state, a single-virus state, and to a coexistence state – the first such result.
(via Semantic Scholar)
arxiv.org (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(OpenAlex)
Source: Web Of Science
Added: November 23, 2021
The dynamics of the spread of contagions such as viruses, infectious diseases or even rumors/opinions over contact networks (graphs) have effectively been captured by the well known Susceptible-Infected-Susceptible (SIS) epidemic model in recent years. When it comes to competition between two such contagions spreading on overlaid graphs, their propagation is captured by so-called bi-virus epidemic models. Analysis of such dynamical systems involve the identification of equilibrium points and its convergence properties, which determine whether either of the viruses dies out, or both survive together. We demonstrate how the existing works are unsuccessful in characterizing a large subset of the model parameter space, including all parameters for which the competitiveness of the bi-virus system is significant enough to attain coexistence of the epidemics. In this paper, we fill in this void and obtain convergence results for the entirety of the model parameter space; giving precise conditions (necessary and sufficient) under which the system globally converges to a trichotomy of possible outcomes: a virus-free state, a single-virus state, and to a coexistence state – the first such result.