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Unformatted text preview: Social Sensing for Epidimiological Behavior Change Anmol Madan, Manuel Cebrian, David Lazer † and Alex Pentland MIT Media Lab and Northeastern University † Cambridge MA anmol, manuel, [email protected]; [email protected] ABSTRACT An important question in behavioral epidemiology and pub- lic health is to understand how individual behavior is af- fected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists to- day do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g. late night, early morning), diversity and entropy of face-to- face interactions and movement. Using these extracted mo- bile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between symptoms, behavior and mental health. Author Keywords Socially aware mobile phones, epidemiology, reality mining. General Terms Algorithms, Design, Documentation, Experimentation, Mea- surement. INTRODUCTION Face-to-face interactions are the primary medium for prop- agation of airborne contagious disease . An important question in behavioral epidemiology and public health is to understand how individual behavior patterns are affected by physical and mental health symptoms. Epidemiologists cur- rently do not have access to sensing and modeling capa- bilities to quantitatively measure behavioral changes expe- rienced by symptomatic individuals in real-world scenarios . Such research requires simultaneously capturing symp- tom reports, mobility patterns and social interactions amongst Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. UbiComp ’10 , Sep 26-Sep 29, 2010, Copenhagen, Denmark. Copyright 2010 ACM 978-1-60558-843-8/10/09...$10.00. individuals continuously over long-term duration. In this pa- per, we propose a novel application of ubiquitous comput- ing, to better understand the link between physical respira- tory symptoms, influenza, stress, mild depression and auto- matically captured behavioral features. This is an important problem in several different ways....
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This note was uploaded on 08/25/2011 for the course EEL 6788 taught by Professor Boloni,l during the Spring '08 term at University of Central Florida.
- Spring '08