A Bayesian Outbreak Detection Method for Influenza-Like Illness

Abstract EN

Epidemic outbreak detection is an important problem in public health and the development of reliable methods foroutbreak detection remains an active research area.

In this paper we introduce a Bayesian method to detect outbreaksof influenza-like illness from surveillance data.

The rationale is that, during the early phase of the outbreak, surveillancedata changes from autoregressive dynamics to a regime of exponential growth.

Our method uses Bayesian modelselection and Bayesian regression to identify the breakpoint.

No free parameters need to be tuned.

However,historical information regarding influenza-like illnesses needs to be incorporated into the model.

In order to show anddiscuss the performance of our method we analyze synthetic, seasonal, and pandemic outbreak data.