Distributed Algorithm for Suppressing Epidemic Spread in Networks

Abstract

This letter considers problems related to suppressing epidemic spread over networks given limited curing resources. The spreading dynamic is captured by a susceptible-infected-susceptible model. The epidemic threshold and recovery speed are determined by the contact network structure and the heterogeneous infection and curing rates. We develop a distributed algorithm that can be used for allocating curing resources to meet three potential objectives: 1) minimize total curing cost while preventing an epidemic; 2) maximize recovery speed given sufficient curing resources; or 3) given insufficient curing resources, limit the size of an endemic state. The distributed algorithm is of the Jacobi type, and converges geometrically. We provide an upper bound on the convergence rate that depends on the structure and infection rates of the underlying network. Numerical simulations illustrate the efficiency and scalability of our distributed algorithm.