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BioMed Research International
Volume 2015 (2015), Article ID 751738, 10 pages
http://dx.doi.org/10.1155/2015/751738
Research Article

A Bayesian Outbreak Detection Method for Influenza-Like Illness

Centro de Investigación en Matemáticas, A.C., Jalisco S/N, Colonia Valenciana, 36240 Guanajuato, GTO, Mexico

Received 27 November 2014; Revised 24 March 2015; Accepted 26 March 2015

Academic Editor: Farai Nyabadza

Copyright © 2015 Yury E. García et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Epidemic outbreak detection is an important problem in public health and the development of reliable methods for outbreak detection remains an active research area. In this paper we introduce a Bayesian method to detect outbreaks of influenza-like illness from surveillance data. The rationale is that, during the early phase of the outbreak, surveillance data changes from autoregressive dynamics to a regime of exponential growth. Our method uses Bayesian model selection 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 and discuss the performance of our method we analyze synthetic, seasonal, and pandemic outbreak data.