  Human mobility is a key component of large-scale spatial-transmission models
of infectious diseases. Correctly modeling and quantifying human mobility is
critical for improving epidemic control policies, but may be hindered by
incomplete data in some regions of the world. Here we explore the opportunity
of using proxy data or models for individual mobility to describe commuting
movements and predict the diffusion of infectious disease. We consider three
European countries and the corresponding commuting networks at different
resolution scales obtained from official census surveys, from proxy data for
human mobility extracted from mobile phone call records, and from the radiation
model calibrated with census data. Metapopulation models defined on the three
countries and integrating the different mobility layers are compared in terms
of epidemic observables. We show that commuting networks from mobile phone data
well capture the empirical commuting patterns, accounting for more than 87% of
the total fluxes. The distributions of commuting fluxes per link from both
sources of data - mobile phones and census - are similar and highly correlated,
however a systematic overestimation of commuting traffic in the mobile phone
data is observed. This leads to epidemics that spread faster than on census
commuting networks, however preserving the order of infection of newly infected
locations. Match in the epidemic invasion pattern is sensitive to initial
conditions: the radiation model shows higher accuracy with respect to mobile
phone data when the seed is central in the network, while the mobile phone
proxy performs better for epidemics seeded in peripheral locations. Results
suggest that different proxies can be used to approximate commuting patterns
across different resolution scales in spatial epidemic simulations, in light of
the desired accuracy in the epidemic outcome under study.
