Revealing latent factors of temporal networks for mesoscale intervention in epidemic spread

Abstract

The customary perspective to reason about epidemic mitigation in temporal networks hinges on the identification of nodes with specific features or network roles. The ensuing individual-based control strategies, however, are difficult to carry out in practice and ignore important correlations between topological and temporal patterns. Here we adopt a mesoscopic perspective and present a principled framework to identify collective features at multiple scales and rank their importance for epidemic spread. We use tensor decomposition techniques to build an additive representation of a temporal network in terms of mesostructures, such as cohesive clusters and temporally-localized mixing patterns. This representation allows to determine the impact of individual mesostructures on epidemic spread and to assess the effect of targeted interventions that remove chosen structures. We illustrate this approach using high-resolution social network data on face-to-face interactions in a school and show that our method affords the design of effective mesoscale interventions.

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Cited by in corpus (7)

• Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
• Compensating for population sampling in simulations of epidemic spread on temporal contact networks
• The structured backbone of temporal social ties
• Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data
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