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Modeling Infection with Multi-agent Dynamics

Author(s)
Dong, Wen; Heller, Katherine; Pentland, Alex Paul
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Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/
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Abstract
Developing the ability to comprehensively study infections in small populations enables us to improve epidemic models and better advise individuals about potential risks to their health. We currently have a limited understanding of how infections spread within a small population because it has been difficult to closely track and infection within a complete community. This paper presents data closely tracking the spread of an infection centered on a student dormitory, collected by leveraging the residents’ use of cellular phones. This data is based on daily symptom surveys taken over a period of four months and proximity tracking through cellular phones. We demonstrate that using a Bayesian, discrete-time multi-agent model of infection to model the real-world symptom report and proximity tracking records can give us important insights about infections in small populations
Date issued
2012
URI
http://hdl.handle.net/1721.1/80416
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Media Laboratory
Journal
Social Computing, Behavioral - Cultural Modeling and Prediction
Publisher
Springer Berlin Heidelberg
Citation
Dong, Wen, Katherine Heller, and Alex Pentland. “Modeling Infection with Multi-agent Dynamics.” Social Computing, Behavioral - Cultural Modeling and Prediction. Ed. Shanchieh Jay Yang, Ariel M. Greenberg, & Mica Endsley. Vol. 7227. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. 172–179.
Version: Author's final manuscript
ISBN
978-3-642-29046-6
978-3-642-29047-3
ISSN
0302-9743
1611-3349

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