Bayesian poisson regression tensor train decomposition model for learning mortality pattern changes during COVID-19 pandemic.

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY
Journal of Applied Statistics Pub Date : 2024-10-10 eCollection Date: 2025-01-01 DOI:10.1080/02664763.2024.2411608
Wei Zhang, Antonietta Mira, Ernst C Wit
{"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.

基于贝叶斯泊松回归张量序列分解模型的COVID-19大流行期间死亡模式变化研究。
COVID-19在世界各地导致了过多的死亡。然而,在此期间,其他死因对死亡率的影响仍不清楚。为了了解COVID-19对其他死亡原因的更广泛影响,我们分析了意大利2015年1月至2020年12月每月死亡人数的官方数据。为了处理数据的高维性质,我们开发了一个将泊松回归与张量序列分解相结合的模型来探索数据的低维剩余结构。我们的贝叶斯方法结合了模型参数的先验信息,并在Gibbs算法中利用高效的Metropolis-Hastings进行后验推理。进行了模拟研究来验证我们的方法。我们的方法不仅通过泊松回归确定了干预措施对病因特异性死亡率的不同影响,而且还提供了对COVID-19与其他死亡原因之间关系的见解。此外,它还揭示了与人口特征、时间模式和死亡原因相关的潜在类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Applied Statistics
Journal of Applied Statistics 数学-统计学与概率论
CiteScore
3.40
自引率
0.00%
发文量
126
审稿时长
6 months
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信