Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis

TL;DR

This paper develops an analytical dynamic message-passing framework to accurately predict the spread of infectious diseases with presymptomatic transmission on contact networks, aiding containment strategy design.

Contribution

It introduces a novel dynamic message-passing method for modeling presymptomatic transmission, improving accuracy over mean-field approaches with lower computational costs.

Findings

Accurately estimates epidemic evolution on networks.

Derives epidemic thresholds for containment.

Informs strategies like social distancing and mask-wearing.

Abstract

Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic.