On the Dynamics of Deterministic Epidemic Propagation over Networks
For researchers studying epidemic spread on networks, this work offers rigorous theoretical foundations and novel computational tools, though it is largely a review with incremental extensions.
This paper provides a comprehensive nonlinear analysis of deterministic epidemic propagation models (SI, SIS, SIR) over strongly-connected networks, establishing equilibria, stability, convergence, and threshold conditions. Novel contributions include computation of the endemic state for SIS and an iterative algorithm for the asymptotic state of SIR.
In this work we review a class of deterministic nonlinear models for the propagation of infectious diseases over contact networks with strongly-connected topologies. We consider network models for susceptible-infected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) settings. In each setting, we provide a comprehensive nonlinear analysis of equilibria, stability properties, convergence, monotonicity, positivity, and threshold conditions. For the network SI setting, specific contributions include establishing its equilibria, stability, and positivity properties. For the network SIS setting, we review a well-known deterministic model, provide novel results on the computation and characterization of the endemic state (when the system is above the epidemic threshold), and present alternative proofs for some of its properties. Finally, for the network SIR setting, we propose novel results for transient behavior, threshold conditions, stability properties, and asymptotic convergence. These results are analogous to those well-known for the scalar case. In addition, we provide a novel iterative algorithm to compute the asymptotic state of the network SIR system.