Estimating global and country-specific excess mortality during the COVID-19 pandemic

DOI10.1214/22-aoas1673arXiv2205.09081OpenAlexW4367598626MaRDI QIDQ6161878

Victoria Knutson, Jon Wakefield, Ariel Karlinsky, William Msemburi, Unnamed Author

Publication date: 5 June 2023

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2205.09081

zbMATH Keywords

Bayesian inferenceglobal healthPoisson frameworksubnational modeling

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Boosting algorithms: regularization, prediction and model fitting
• Bootstrapping general empirical measures
• High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}
• Penalising model component complexity: a principled, practical approach to constructing priors
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Frequency Domain Log-linear Models; Air Pollution and Mortality
• Gaussian Markov Random Fields
• Super Learner

This page was built for publication: Estimating global and country-specific excess mortality during the COVID-19 pandemic