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The integration of empirical data in computational frameworks to model thespread of infectious diseases poses challenges that are becoming pressing withthe increasing availability of high-resolution information on human mobilityand contacts. This deluge of data has the potential to revolutionize thecomputational efforts aimed at simulating scenarios and designing containmentstrategies. However, the integration of detailed data sources yields modelsthat are less transparent and general. Hence, given a specific disease model,it is crucial to assess which representations of the raw data strike the bestbalance between simplicity and detail. We consider high-resolution data on theface-to-face interactions of individuals in a hospital ward, obtained by usingwearable proximity sensors. We simulate the spread of a disease in thiscommunity by using an SEIR model on top of different mathematicalrepresentations of the contact patterns. We show that a contact matrix thatonly contains average contact durations fails to reproduce the size of theepidemic obtained with the high-resolution contact data and also to identifythe most at-risk classes. We introduce a contact matrix of probabilitydistributions that takes into account the heterogeneity of contact durationsbetween (and within) classes of individuals, and we show that thisrepresentation yields a good approximation of the epidemic spreading propertiesobtained by using the high-resolution data. Our results mark a step towards thedefinition of synopses of high-resolution dynamic contact networks, providing acompact representation of contact patterns that can correctly informcomputational models designed to discover risk groups and evaluate containmentpolicies. We show that this novel kind of representation can preserve insimulation quantitative features of the epidemics that are crucial for theirstudy and management.

An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices