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R Soc Open Sci. 2017 May 17;4(5):160950. doi: 10.1098/rsos.160950. eCollection 2017 May.

Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models.

Author information

Dipartimento di Fisica, Università degli Studi di Torino, via Giuria 1, Torino 10125, Italy.
ISI Foundation, via Alassio 11/C, Torino 10126, Italy.
Aizoon Technology Consulting, Str. del Lionetto 6, Torino, Italy.
Sociology and Economics of Networks and Services Department, Orange Laboratories, Issy-les-Moulineaux, France.
Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP, UMR-S 1136), Paris, France.


The 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.


epidemic modelling; infectious diseases; mobile phones; spatial epidemiology

Conflict of interest statement

The authors declare they have no competing interests.

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