The basic reproduction number as a predictor for epidemic outbreaks in temporal networks

TL;DR

This study investigates how the basic reproduction number R0 predicts the final epidemic size {} in empirical temporal networks, revealing that network structure influences this relationship.

Contribution

It identifies specific temporal and topological network descriptors that affect the predictive power of R0 for epidemic outcomes.

Findings

R0 is an imperfect predictor of final epidemic size {} in temporal networks.

Certain network descriptors significantly influence the R0-{} relationship.

Temporal and topological features impact epidemic spread predictions.

Abstract

The basic reproduction number R0 -- the number of individuals directly infected by an infectious person in an otherwise susceptible population -- is arguably the most widely used estimator of how severe an epidemic outbreak can be. This severity can be more directly measured as the fraction people infected once the outbreak is over, {\Omega}. In traditional mathematical epidemiology and common formulations of static network epidemiology, there is a deterministic relationship between R0 and {\Omega}. However, if one considers disease spreading on a temporal contact network -- where one knows when contacts happen, not only between whom -- then larger R0 does not necessarily imply larger {\Omega}. In this paper, we numerically investigate the relationship between R0 and {\Omega} for a set of empirical temporal networks of human contacts. Among 31 explanatory descriptors of temporal network structure, we identify those that make R0 an imperfect predictor of {\Omega}. We find that descriptors related to both temporal and topological aspects affect the relationship between R0 and {\Omega}, but in different ways.