Epidemic prediction and control in clustered populations

TL;DR

This paper develops pairwise methods to predict epidemic outcomes and intervention needs in clustered populations, revealing which predictions are robust or sensitive to clustering effects.

Contribution

It introduces pairwise modeling techniques for epidemic prediction in clustered networks, highlighting the impact of clustering on various epidemic metrics.

Findings

Early outbreak growth predicts vaccine requirements and peak timing.

Basic reproductive ratio predicts vaccination threshold.

Some epidemic predictions are unaffected by clustering, others are highly sensitive.

Abstract

There has been much recent interest in modelling epidemics on networks, particularly in the presence of substantial clustering. Here, we develop pairwise methods to answer questions that are often addressed using epidemic models, in particular: on the basis of potential observations early in an outbreak, what can be predicted about the epidemic outcomes and the levels of intervention necessary to control the epidemic? We find that while some results are independent of the level of clustering (early growth predicts the level of `leaky' vaccine needed for control and peak time, while the basic reproductive ratio predicts the random vaccination threshold) the relationship between other quantities is very sensitive to clustering.