title: A Bayesian Outbreak Detection Method for Influenza-Like Illness.
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.
authors.affiliation: Centro de Investigación en Matemáticas, A.C., Jalisco S/N, Colonia Valenciana, 36240 Guanajuato, GTO, Mexico.
authors.forename: Yury E
authors.initials: YE
authors.lastname: García
authors.affiliation: Centro de Investigación en Matemáticas, A.C., Jalisco S/N, Colonia Valenciana, 36240 Guanajuato, GTO, Mexico.
authors.forename: J Andrés
authors.initials: JA
authors.lastname: Christen
authors.affiliation: Centro de Investigación en Matemáticas, A.C., Jalisco S/N, Colonia Valenciana, 36240 Guanajuato, GTO, Mexico.
authors.forename: Marcos A
authors.initials: MA
authors.lastname: Capistrán
mesh_terms: D001499:Bayes Theorem; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D015233:Models, Statistical; D011156:Population Density; D012495:San Francisco / Q000453:epidemiology; D013030:Spain; D013223:Statistics as Topic