Epidemics in networks with nodal self-infection and the epidemic threshold

P Van Mieghem, E Cator - Physical Review E—Statistical, Nonlinear, and Soft …, 2012 - APS
Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 2012APS
Since the Susceptible-Infected-Susceptible (SIS) epidemic threshold is not precisely defined
in spite of its practical importance, the classical SIS epidemic process has been generalized
to the ɛ− SIS model, where a node possesses a self-infection rate ɛ, in addition to a link
infection rate β and a curing rate δ. The exact Markov equations are derived, from which the
steady state can be computed. The major advantage of the ɛ− SIS model is that its steady
state is different from the absorbing (or overall-healthy state) and approximates, for a certain …
Since the Susceptible-Infected-Susceptible (SIS) epidemic threshold is not precisely defined in spite of its practical importance, the classical SIS epidemic process has been generalized to the ɛ−SIS model, where a node possesses a self-infection rate ɛ, in addition to a link infection rate and a curing rate . The exact Markov equations are derived, from which the steady state can be computed. The major advantage of the ɛ−SIS model is that its steady state is different from the absorbing (or overall-healthy state) and approximates, for a certain range of small ɛ>0, the in reality observed phase transition, also called the “metastable” state, that is characterized by the epidemic threshold. The exact steady-state analysis for the complete graph illustrates the effect of small ɛ and the quality of the first-order mean-field approximation, the -intertwined model, proposed earlier. Apart from duality principles, often used in the mathematical literature, we present an exact recursion relation for the Markov infinitesimal generator.
American Physical Society
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