  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.
