Mitigating an epidemic on a geographic network using vaccination

TL;DR

This paper develops a mathematical framework to optimize vaccination strategies on geographic networks by analyzing epidemic growth through eigenvalues, providing practical guidelines for epidemic control.

Contribution

It introduces a novel eigenvector-based vaccination strategy derived from matrix perturbation theory for epidemic mitigation on networks.

Findings

Vaccinating along the Laplacian eigenvector is most effective when mobility and disease dynamics are similar.

Targeting high-degree vertices reduces epidemic growth more effectively.

Slower mobility shifts focus to vertices with the largest susceptibles.

Abstract

We consider a mathematical model describing the propagation of an epidemic on a geographical network. The size of the outbreak is governed by the initial growth rate of the disease given by the maximal eigenvalue of the epidemic matrix formed by the susceptibles and the graph Laplacian representing the mobility. We use matrix perturbation theory to analyze the epidemic matrix and define a vaccination strategy, assuming the vaccination reduces the susceptibles. When mobility and local disease dynamics have similar time scales, it is most efficient to vaccinate the whole network because the disease grows uniformly. However, if only a few vertices can be vaccinated then which ones do we choose? We answer this question, and show that it is most efficient to vaccinate along an eigenvector corresponding to the largest eigenvalue of the Laplacian. We illustrate these general results on a 7 vertex graph and a realistic example of the french rail network. When mobility is slower than local disease dynamics, the epidemic grows on the vertex with largest susceptibles. The epidemic growth rate is more reduced when vaccinating a larger degree vertex; it also depends on the neighboring vertices. This study and its conclusions provides guidelines for the planning of vaccination on a network at the onset of an epidemic.