18 citations · 26 across the 5 of their papers we have counts for
5 papers
Spatio-temporal stochastic graph-based learning for infectious disease forecasting
Luz Stefani Sotomayor Valenzuela, Susanna Cramb, Darren Wraith
Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. However, the use of stochastic m…
Creating area level indices of behaviours impacting cancer in Australia with a Bayesian generalised shared component model
James Hogg, Susanna Cramb, Jessica Cameron +2
This study develops a model-based index creation approach called the Generalized Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Ba…
Mapping the prevalence of cancer risk factors at the small area level in Australia
James Hogg, Jessica Cameron, Susanna Cramb +2
Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area level data on cancer ri…
A Two-Stage Bayesian Small Area Estimation Approach for Proportions
James Hogg, Jessica Cameron, Susanna Cramb +2
With the rise in popularity of digital Atlases to communicate spatial variation, there is an increasing need for robust small-area estimates. However, current small-area estimation…
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Joshua J. Bon, Adam Bretherton, Katie Buchhorn +12
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most sta…