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Communication Dans Un Congrès Année : 2015

Predicting a Community's Flu Dynamics with Mobile Phone Data

Résumé

Human interactions that are sensed ubiquitously by mobile phones can improve a significant number of public health problems, particularly helping to track the spread of disease. In this paper, we evaluate multiple avenues for the integration of high-resolution face to face Bluetooth-sensed interaction networks into standard epidemic models. Our goal is to evaluate the capacity of the different avenues of integration to track the spread of seasonal influenza on a real-world community of 72 individuals over a period of 17 weeks. The dataset considered contains real-time tracking of individual flu symptoms over the whole observation period, providing a concrete individualized source for evaluation. We obtain an error of less than 2 infected people on average for predicting the total number of individuals affected by the flu and precision of approximately 30% when predicting exactly which individual will become infected at a given time. To the best of our knowledge, this is the first study considering mobile phone Bluetooth-sensed interaction data for dynamic infectious disease simulation that is evaluated against real human influenza occurrence. Our remarkable results indicate that high-resolution mobile phone data can increase the predictive power of even the simplest of epidemic models.
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Dates et versions

hal-01146198 , version 1 (28-04-2015)

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Katayoun Farrahi, Rémi Emonet, Manuel Cebrian. Predicting a Community's Flu Dynamics with Mobile Phone Data. Computer-Supported Cooperative Work and Social Computing, Mar 2015, Vancouver, Canada. ⟨10.1145/2675133.2675237⟩. ⟨hal-01146198⟩
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