Information content of contact-pattern representations and predictability of epidemic outbreaks

TL;DR

This paper examines how different levels of contact pattern detail, from random mixing to temporal networks, affect epidemic outbreak predictions using empirical data, highlighting the importance of temporal structure in disease modeling.

Contribution

It compares epidemic outbreak predictions across models with increasing contact information, emphasizing the impact of temporal network structure on outbreak dynamics.

Findings

Large differences in outbreak sizes when moving from fully mixed to network models.

Smaller differences when incorporating temporal information in outbreak predictions.

Fast-changing contact networks increase the importance of temporal data.

Abstract

To understand the contact patterns of a population -- who is in contact with whom, and when the contacts happen -- is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important.