Tuesday 7 October 10:50 a.m. - 12:30 p.m. (Europe/Amsterdam)
Abstract
When the number of cases of a disease is recorded across different areas within a region, disease mapping is useful to estimate the areal relative risk. The number of cases in an area is often assumed to follow a Poisson distribution whose log risk may be written as the sum of fixed and random effects. The BYM2 model decomposes each latent effect into a weighted sum of independent and spatial effects. We extend the BYM2 model to allow for heavy-tailed latent effects and accommodate potentially outlying risks, after accounting for the fixed effects. We assume a scale mixture wherein the variance of the latent process changes across areas and allows for outlier identification. We explore two prior specifications of the scaling parameters and compare the proposed model to another proposal in the literature, in simulation studies and in the analysis of Zika cases from the 2015-2016 epidemic in Rio de Janeiro. Further, we extend a spatio-temporal model to allow for heavy-tailed effects to accommodate and identify outliers. At each time point, we assume the latent effects to be spatially structured and include scaling parameters in the precision matrix to allow for heavy tails. We investigate the performance of the proposed model through simulation studies and analyse the weekly evolution of COVID-19 across the neighborhoods of Montreal.
