An Agent-Based Distributed Control of Networked SIR Epidemics

TL;DR

This paper proposes a simple, threshold-based control strategy for managing epidemic spread in networks, using local information and mean-field approximation to ensure safety while maximizing social interaction.

Contribution

It introduces a novel, practical control approach based on local data and mean-field methods, avoiding intractable optimization of complex epidemic policies.

Findings

Control strategy is a threshold on infection probability of neighbors.

Simulations demonstrate effectiveness of the proposed method.

Approach balances social activity with safety constraints.

Abstract

This paper revisits a longstanding problem of interest concerning the distributed control of an epidemic process on human contact networks. Due to the stochastic nature and combinatorial complexity of the problem, finding optimal policies are intractable even for small networks. Even if a solution could be found efficiently enough, a potentially larger problem is such policies are notoriously brittle when confronted with small disturbances or uncooperative agents in the network. Unlike the vast majority of related works in this area, we circumvent the goal of directly solving the intractable and instead seek simple control strategies to address this problem. More specifically, based on the locally available information to a particular person, how should that person make use of this information to socialize as much as possible while ensuring some desired level of safety? We set this up as a finite time optimal control problem using a well known exact Markov chain Susceptible-Infected-Removed (SIR) model. Leveraging results from the literature, we employ a commonly used mean-field approximation (MFA) technique to relax the problem. We show solution of this problem to be a form of threshold on the chance of infection of the neighbors of that person. Simulations illustrate our results.