{"title":"Bayesian poisson regression tensor train decomposition model for learning mortality pattern changes during COVID-19 pandemic.","authors":"Wei Zhang, Antonietta Mira, Ernst C Wit","doi":"10.1080/02664763.2024.2411608","DOIUrl":null,"url":null,"abstract":"<p><p>COVID-19 has led to excess deaths around the world. However, the impact on mortality rates from other causes of death during this time remains unclear. To understand the broader impact of COVID-19 on other causes of death, we analyze Italian official data covering monthly mortality counts from January 2015 to December 2020. To handle the high-dimensional nature of the data, we developed a model that combines Poisson regression with tensor train decomposition to explore the lower-dimensional residual structure of the data. Our Bayesian approach incorporates prior information on model parameters and utilizes an efficient Metropolis-Hastings within Gibbs algorithm for posterior inference. Simulation studies were conducted to validate our approach. Our method not only identifies differential effects of interventions on cause-specific mortality rates through Poisson regression but also provides insights into the relationship between COVID-19 and other causes of death. Additionally, it uncovers latent classes related to demographic characteristics, temporal patterns, and causes of death.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"1017-1039"},"PeriodicalIF":1.2000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951336/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/02664763.2024.2411608","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
COVID-19 has led to excess deaths around the world. However, the impact on mortality rates from other causes of death during this time remains unclear. To understand the broader impact of COVID-19 on other causes of death, we analyze Italian official data covering monthly mortality counts from January 2015 to December 2020. To handle the high-dimensional nature of the data, we developed a model that combines Poisson regression with tensor train decomposition to explore the lower-dimensional residual structure of the data. Our Bayesian approach incorporates prior information on model parameters and utilizes an efficient Metropolis-Hastings within Gibbs algorithm for posterior inference. Simulation studies were conducted to validate our approach. Our method not only identifies differential effects of interventions on cause-specific mortality rates through Poisson regression but also provides insights into the relationship between COVID-19 and other causes of death. Additionally, it uncovers latent classes related to demographic characteristics, temporal patterns, and causes of death.
期刊介绍:
Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees.