Cassese, AlbertoZhu, WeixuanGuindani, MicheleVannucci, Marina2021-12-172021-12-172019Cassese, Alberto, Zhu, Weixuan, Guindani, Michele, et al.. "A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection." <i>Bayesian Analysis,</i> 14, no. 2 (2019) Project Euclid: 553-572. https://doi.org/10.1214/18-BA1116.https://hdl.handle.net/1911/111874In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or “normal” behavior. In this manuscript, we consider the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States, and propose a Bayesian nonparametric model selection approach to take into account the spatio-temporal dependence of outbreaks. More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations. We show how the proposed modeling framework performs in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for detecting outbreaks over time, with more recent Markov switching based models, and with spike-and-slab Bayesian nonparametric priors that do not take into account spatio-temporal dependence.engCreative Commons Attribution 4.0 International LicenseJournal article18-BA1116https://doi.org/10.1214/18-BA1116