A Bayesian approach for detecting a disease that is not being modeled

Aronis, John M.; Ferraro, Jeffrey P.; Gesteland, Per H.; Tsui, Fuchiang; Ye, Ye; Wagner, Michael M.; Cooper, Gregory F.

Outbreaks of new (or old) diseases typically display an initial exponential rate of growth [14]. DUDE compares the set of cases in the monitor window with a baseline set of cases and generates an alarm if the presence of an unmodeled disease better explains the difference than simple variation. However, this ignores the daily increase in the number of cases of the unmodeled disease. It would be better if DUDE generated an alarm when it detects an increasing number of cases of an unmodeled disease. Doing so would increase the computational complexity since it will need to search over (the parameters of) a set of increasing curves and their start dates, but this can be ameliorated by heuristics that favor increasing disease curves that match increasing numbers of findings.