Bayesian Contact Tracing for Communicable Respiratory Disease

Authors

  • Ayman Shalaby University of Waterloo
  • Daniel Lizotte University of Waterloo

DOI:

https://doi.org/10.5210/ojphi.v5i1.4574

Abstract

The purpose of our work is to develop a system for automatic contact tracing with the goal of identifying individuals who are most likely infected, even if we do not have direct diagnostic information on their health status. We developed a dynamic Bayesian network to process the sensors information from users' cellphones to track the spreading of the pandemic in the population. Our Bayesian data analysis algorithms track the real-time proximity contacts in the population and provide the public health agencies, the probabilistic likelihood for each individual of being infected by the novel virus.

Author Biography

Ayman Shalaby, University of Waterloo

Ayman Shalaby received his bachelor's and master's degrees in electrical engineering (with highest honors) from Alexandria University and University of Toronto, respectively. He is currently a graduate student at the school of computer science at the University of Waterloo. His current research interest spans different areas such as machine learning, big data analytics, bayesian non-parametric models, etc.

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Published

2013-03-24

How to Cite

Shalaby, A., & Lizotte, D. (2013). Bayesian Contact Tracing for Communicable Respiratory Disease. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4574

Issue

Vol. 5 No. 1 (2013)

Section

System Showcase Demonstrations

License

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