PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases

Individual camera device identification from JPEG images

International audienceThe goal of this paper is to investigate the problem of source camera device identification for natural images in JPEG format. We propose an improved signal-dependent noise model describing the statistical distribution of pixels from a JPEG image. The noise model relies on the heteroscedastic noise parameters, that relates the variance of pixels’ noise with the expectation considered as unique fingerprints. It is also shown in the present paper that, non-linear response of pixels can be captured by characterizing the linear relation because those heteroscedastic parameters, which are used to identify source camera device. The identification problem is cast within the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test (LRT) is presented and its performance is theoretically established. The statistical performance of LRT serves as an upper bound of the detection power. In a practical identification, when the nuisance parameters are unknown, two generalized LRTs based on estimation of those parameters are established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach. While those results show a first positive proof of concept of the method, it still requires to be extended for a relevant comparison with PRNU-based approaches that benefit from years of experience