Bayesian modeling of unknown disease for biosurveillance
Shen Y, Cooper GF. Bayesian modeling of unknown disease for biosurveillance. In: Proceedings of the Annual Symposium of the American Medical Informatics Association (2009) 589-593. PMID: 20351923 PMC2815446.
This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this paper is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in medical informatics, where the space of known causes of outcomes of interest is seldom complete.