Bayesian Contact Tracing for Communicable Respiratory Disease

Ayman Shalaby, Daniel Lizotte


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.

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Online Journal of Public Health Informatics * ISSN 1947-2579 *