Predicting epidemics on weighted networks

TL;DR

This paper introduces a new framework to analyze weighted contact networks, enabling more accurate epidemiological predictions by incorporating heterogeneity in contact intensity and structure.

Contribution

The authors develop a novel analytical framework to estimate epidemic dynamics from weighted networks, addressing a key gap in existing models that ignore contact heterogeneity.

Findings

Framework accurately predicts early epidemic growth rates.

Validates epidemic prevalence trajectories with simulations.

Enhances modeling realism for disease spread predictions.

Abstract

The contact structure between hosts has a critical influence on disease spread. However, most networkbased models used in epidemiology tend to ignore heterogeneity in the weighting of contacts. This assumption is known to be at odds with the data for many contact networks (e.g. sexual contact networks) and to have a strong effect on the predictions of epidemiological models. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion and the basic reproductive ratio, from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. This framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour which is validated using numerical simulations. Our framework allows for the incorporation of more realistic contact networks into epidemiological models, thus improving predictions on the spread of emerging infectious diseases.