The integration of empirical data in computational frameworks to model the
spread of infectious diseases poses challenges that are becoming pressing with
the increasing availability of high-resolution information on human mobility
and contacts. This deluge of data has the potential to revolutionize the
computational efforts aimed at simulating scenarios and designing containment
strategies. However, the integration of detailed data sources yields models
that are less transparent and general. Hence, given a specific disease model,
it is crucial to assess which representations of the raw data strike the best
balance between simplicity and detail. We consider high-resolution data on the
face-to-face interactions of individuals in a hospital ward, obtained by using
wearable proximity sensors. We simulate the spread of a disease in this
community by using an SEIR model on top of different mathematical
representations of the contact patterns. We show that a contact matrix that
only contains average contact durations fails to reproduce the size of the
epidemic obtained with the high-resolution contact data and also to identify
the most at-risk classes. We introduce a contact matrix of probability
distributions that takes into account the heterogeneity of contact durations
between (and within) classes of individuals, and we show that this
representation yields a good approximation of the epidemic spreading properties
obtained by using the high-resolution data. Our results mark a step towards the
definition of synopses of high-resolution dynamic contact networks, providing a
compact representation of contact patterns that can correctly inform
computational models designed to discover risk groups and evaluate containment
policies. We show that this novel kind of representation can preserve in
simulation quantitative features of the epidemics that are crucial for their
study and management