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Social sensing for epidemiological behavior change
Open accessAbstract
An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today 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 mobile 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 physical symptoms, behavior and mental health.
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“Individual mobility behaviour changes in fever, influenza or mental stress situations [116].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
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“Finally the health status of an individual is predicted with a Bayesian network classifier, without having actual health measurements from the subjects.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
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“Behavioural changes are reflected in 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.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
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Huang, W., Kuo, Y.-S., Pannuto, P., & Dutta, P. K. (2014) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2668332.2668338 · Cited by 28
A survey on predicting personal mobility
Burbey, I., & Martin, T. L. (2012) · International Journal of Pervasive Computing and Communications · doi:10.1108/17427371211221063 · Cited by 25
The Mobile Territorial Lab: a multilayered and dynamic view on parents’ daily lives
Centellegher, S., Nadai, M. D., Caraviello, M., Leonardi, C., Vescovi, M., Ramadian, Y., Oliver, N., Pianesi, F., Pentland, A., Antonelli, F., et al. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0064-6 · Cited by 25
Self-citationGasparetti, F. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.04.004 · Cited by 26
Network Diversity and Affect Dynamics: The Role of Personality Traits
Alshamsi, A., Pianesi, F., Lepri, B., Pentland, A., & Rahwan, I. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152358 · Cited by 26
Self-citationDeiner, M. S., McLeod, S. D., Chodosh, J., Oldenburg, C. E., Fathy, C. A., Lietman, T. M., & Porco, T. C. (2018) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.17-22818 · Cited by 26
Measuring social integration and tie strength with smartphone and survey data
Dissing, A. S., Lakon, C. M., Gerds, T. A., Rod, N. H., & Lund, R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200678 · Cited by 26
Modeling Infection with Multi-agent Dynamics
Dong, W., Heller, K., & Pentl, Y. (2012) · Cited by 26
How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage
Altshuler, Y., Fire, M., Aharony, N., Elovici, Y., & Pentland, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29047-3_6 · Cited by 25
Self-citationRobust Modeling of Human Contact Networks Across Different Scales and Proximity-Sensing Techniques
Starnini, M., Lepri, B., Baronchelli, A., Barrat, A., Cattuto, C., & Pastor‐Satorras, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67217-5_32 · Cited by 25
Detecting Conversing Groups Using Social Dynamics from Wearable Acceleration
Gedik, E., & Hung, H. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287041 · Cited by 23
Opportunistic natural experiments using digital telemetry: a transit disruption case study
Stanley, K. G., Bell, S., Kreuger, L. K., Bhowmik, P., Shojaati, N., Elliott, A., & Osgood, N. D. (2016) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2016.1145224 · Cited by 24
Hwang, I., Jang, H., Park, T., Choi, A., Lee, Y., Hwang, C., Choi, Y., Nachman, L., & Song, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31205-2_3 · Cited by 26
Metadata
- Authors
- Madan, A., Cebrian, M., Lazer, D., & Pentland, A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard University (US)
- Venue
- Proceedings of the 12th ACM international conference on Ubiquitous computing
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 291-300
- OpenAlex
- W2171436624
- Cited by
- 219
- References
- 28
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 6 · 2022: 7 · 2021: 14 · 2020: 14 · 2019: 14 · 2018: 17 · 2017: 15 · 2016: 24 · 2015: 22 · 2014: 19 · 2013: 20 · 2012: 25
- FWCI
- 42.39
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- social sensing, behavioral epidemiology, mobile phone data, face-to-face interaction, movement patterns, health prediction, mental health, ubiquitous computing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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