02036nas a2200169   4500008004100000245011000041210006900151300001100220490000600231520139500237100002301632700002101655700001901676700002201695700002201717856012701739       2017                        eng d00aAssessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models0 aAssessing the use of mobile phone data to describe recurrent mob  a1609500 v43 aThe recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.1 aPanigutti, Cecilia1 aTizzoni, Michele1 aBajardi, Paolo1 aSmoreda, Zbigniew1 aColizza, Vittoria  uhttps://kdd.isti.cnr.it/publications/assessing-use-mobile-phone-data-describe-recurrent-mobility-patterns-spatial-epidemic