Efficient Containment of Exact SIR Markovian Processes on Networks

TL;DR

This paper develops a precise, convex optimization-based method for controlling the spread of infections in networked SIR models without mean-field approximations, demonstrated through numerical simulations.

Contribution

It introduces a novel convex optimization framework for exact SIR process analysis and containment on networks, avoiding mean-field approximations.

Findings

Effective resource allocation reduces infection spread.

Convex optimization provides efficient containment strategies.

Numerical results validate the theoretical approach.

Abstract

This paper introduces a theoretical framework for the analysis and control of the stochastic susceptible-infected-removed (SIR) spreading process over a network of heterogeneous agents. In our analysis, we analyze the exact networked Markov process describing the SIR model, without resorting to mean-field approximations, and introduce a convex optimization framework to find an efficient allocation of resources to contain the expected number of accumulated infections over time. Numerical simulations are presented to illustrate the effectiveness of the obtained results.