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
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© 2012 Springer-Verlag Berlin Heidelberg
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Dong, W., Heller, K., Pentland, A.(. (2012). Modeling Infection with Multi-agent Dynamics. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds) Social Computing, Behavioral - Cultural Modeling and Prediction. SBP 2012. Lecture Notes in Computer Science, vol 7227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29047-3_21
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DOI: https://doi.org/10.1007/978-3-642-29047-3_21
Publisher Name: Springer, Berlin, Heidelberg
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