Detecting disease outbreaks using a combined Bayesian network and particle filter approach.

Evaluating whether a disease outbreak has occurred based on limited information in medical records is inherently a probabilistic problem. This paper presents a methodology for consistently analysing the probability that a disease targeted by a surveillance system has appeared in the population, based on the medical records of the individuals within the target population, using a Bayesian network. To enable the system to produce a probability density function of the fraction of the population that is infected, a mathematically consistent conjoining of Bayesian networks and particle filters is used. This approach is tested against the default algorithm of ESSENCE Desktop Edition (which adaptively uses Poisson, exponentially weighted moving average and linear regression techniques as needed), and is shown, for the simulated test data used, to give significantly shorter detection times at false alarm rates of practical interest. This methodology shows promise to greatly improve detection times for outbreaks in populations where timely electronic health records are available for data-mining.