计数数据的贝叶斯核机回归:模拟南卡罗来纳州社会脆弱性与COVID-19死亡之间的关系

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Fedelis Mutiso, Hong Li, John L Pearce, Sara E Benjamin-Neelon, Noel T Mueller, Brian Neelon
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引用次数: 0

摘要

新冠肺炎大流行造成了前所未有的全球卫生危机。最近的研究表明,社会弱势群体受到了不成比例的影响,尽管结果好坏参半。为了量化美国的社会脆弱性,许多研究都依赖于社会脆弱性指数(SVI),这是一个由15个人口普查变量组成的县级衡量指标。通常,SVI以相加的方式建模,这可能会模糊非线性或交互关联,进一步导致不一致的结果。作为一个更稳健的替代方案,我们提出了一个负二项贝叶斯核机回归(BKMR)模型来研究社会脆弱性与COVID-19死亡率之间的动态关联,从而将BKMR扩展到计数数据设置。该模型产生了“脆弱性效应”,量化了脆弱性对每个县COVID-19死亡率的影响。该方法还可以识别各种SVI变量的相对重要性,并根据县脆弱性特征的演变进行未来预测。为了捕捉时空异质性,该模型结合了空间效应、县级协变量和平滑时间函数。对于贝叶斯计算,我们提出了一种易于处理的数据增强吉布斯采样器。我们进行了一项模拟研究,以突出该方法,并将该方法应用于2021年美国南卡罗来纳州COVID-19死亡的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina
Abstract The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a ‘vulnerability effect’ that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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