2 research outputs found
Bayesian modeling and clustering for spatio-temporal areal data: An application to Italian unemployment
Spatio-temporal areal data can be seen as a collection of time series which
are spatially correlated according to a specific neighboring structure.
Incorporating the temporal and spatial dimension into a statistical model poses
challenges regarding the underlying theoretical framework as well as the
implementation of efficient computational methods. We propose to include
spatio-temporal random effects using a conditional autoregressive prior, where
the temporal correlation is modeled through an autoregressive mean
decomposition and the spatial correlation by the precision matrix inheriting
the neighboring structure. Their joint distribution constitutes a Gaussian
Markov random field, whose sparse precision matrix enables the usage of
efficient sampling algorithms. We cluster the areal units using a nonparametric
prior, thereby learning latent partitions of the areal units. The performance
of the model is assessed via an application to study regional unemployment
patterns in Italy. When compared to other spatial and spatio-temporal
competitors, the proposed model shows more precise estimates and the additional
information obtained from the clustering allows for an extended economic
interpretation of the unemployment rates of the Italian provinces
Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission
AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p