Bayesian Modelling Frameworks for Under-Reporting and Delayed Reporting in Count Data

Oliver Stoner (University of Exeter)

Friday 25th October 15:00-16:00 Maths 311B

Abstract

In practical applications, including disease surveillance and monitoring severe weather events, available count data are often an incomplete representation of phenomena we are interested in. This includes under-reporting, where observed counts are thought to be less than or equal to the truth, and delayed reporting, where total counts are not immediately available, instead arriving in parts over time.

In this seminar I will present two Bayesian hierarchical modelling frameworks which aim to deal with each of these issues, respectively. I will also present applications to UK tornado data, where under-reporting is thought to be a problem in areas of low population density, and to dengue fever data from Brazil, where notification delay means that the true size of outbreaks may not be known for weeks or even months after they've occurred.

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