Modeling the co-evolution of behaviors and social relationships using mobile phone data
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Modeling infection with multi-agent dynamics
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Modeling infection with multi-agent dynamics
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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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