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Six challenges in measuring contact networks for use in modelling
Author(s) -
Ken Eames,
Shweta Bansal,
Simon D. W. Frost,
Steven Riley
Publication year - 2014
Publication title -
epidemics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.023
H-Index - 41
eISSN - 1755-4365
pISSN - 1878-0067
DOI - 10.1016/j.epidem.2014.08.006
Subject(s) - data science , bounding overwatch , population , data collection , proxy (statistics) , computer science , scope (computer science) , artificial intelligence , machine learning , medicine , sociology , social science , environmental health , programming language
Contact networks are playing an increasingly important role in epidemiology. A contact network represents individuals in a host population as nodes and the interactions among them that may lead to the transmission of infection as edges. New avenues for data collection in recent years have afforded us the opportunity to collect individual- and population-scale information to empirically describe the patterns of contact within host populations. Here, we present some of the current challenges in measuring empirical contact networks. We address fundamental questions such as defining contact; measurement of non-trivial contact properties; practical issues of bounding measurement of contact networks in space, time and scope; exploiting proxy information about contacts; dealing with missing data. Finally, we consider the privacy and ethical issues surrounding the collection of contact network data.

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