Reducing Recurrent Competitive Epidemics via Dynamic Resource Allocation

TL;DR

This paper introduces gLRIE, a dynamic resource allocation strategy designed to control competing epidemic processes on social networks, demonstrating its effectiveness through theoretical analysis and simulations.

Contribution

It generalizes the LRIE strategy to a continuous-time SIS model with competing states, providing a greedy approach for epidemic mitigation.

Findings

gLRIE effectively reduces the number of infected nodes over time.

The strategy outperforms existing methods in various simulated scenarios.

Demonstrated success in a realistic counter-contagion campaign.

Abstract

Motivated by scenarios of epidemic competition, as well as how social contagions spread at the level of individuals, this work considers the competition between two conflicting node states that spread over a social graph according to a generic diffusion process. For this setting, we introduce the Generalized Largest Reduction in Infectious Edges (gLRIE), which is a dynamic resource allocation strategy that favors the preferred state against the other. Our analysis assumes a generic continuous-time SIS-like (Susceptible-Infectious-Susceptible) diffusion model that allows for: arbitrary node transition rate functions for nodes to change state, and competition between the healthy (positive) and infected (negative) states, which are both diffusive at the same time, yet mutually exclusive at each node. The strategy follows a minimum-risk-maximum-gain principle, and its features are particularly relevant for social contagion phenomena. In accordance with the LRIE strategy that we generalize, we show that in this context the gLRIE strategy remains a greedy solution for the minimization of the number of infected network nodes over time. Ultimately, simulations are employed to compare the proposed strategy with other existing alternatives, demonstrating that gLRIE exhibits superior performance across a spectrum of scenarios, including a realistic counter-contagion campaign in a small well-monitored community.