This thesis presents an analysis of the epidemic processes evolving over underlying networks structures, specifically, considering an epidemic compartment model, SAIRS, that addresses the role of the asymptomatic infected subgroup of the population. Asymptomatic carriers are crucial to the epidemic spread process owning to their capacity to infect susceptible individuals, whereas it is a challenge to detect, monitor and further impose control policies on this subpopulation. This thesis first discusses the background of classic epidemics models and common converging thresholds, and then presents group SAIRS model and its networked version N-SAIRS. Equilibria and stability properties for these models are investigated. After estimating model parameters from local test-sites data with simple least-square approaches, two simulations are presented. One illustrates the effects of asymptomatic-infected individuals on the epidemic spread process with different level of control policies, the other shows the influence of different underlying network structures on the epidemic evolution in terms of networked models and the impact of local isolation on the epidemic dynamics.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
