A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities
A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities
复制标题
COVID-19 死亡率和人口合并症的混合回归模型
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
S. Pyne
中科院分区：
文献类型：
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作者：
M. Maleki;G. McLachlan;R. Gurewitsch;M. Aruru;S. Pyne
随着COVID-19疫情在全球蔓延，越来越明显的是，
特定人群中的某些合并症可能使其更容易发生严重后果
包括死亡率事实上，从卫生政策的角度来看，这可能很有见地
根据其流行病之间的关联来识别人群集群，
合并症和观察到的COVID-19特定死亡率。在这项研究中，我们描述了一种
混合多项式时间序列（MoPTS）模型，以同时识别（a）三个集群
86个美国城市的动态死亡率，以及（B）
这些比率在集群中的人群中具有5种关键合并症。我们还描述
EM算法的有效最大似然估计的模型参数。
As the COVID-19 pandemic spread worldwide, it has become clearer that prevalence of
certain comorbidities in a given population could make it more vulnerable to serious outcomes
of that disease, including fatality. Indeed, it might be insightful from a health policy perspective
to identify clusters of populations in terms of the associations between their prevalent
comorbidities and the observed COVID-19 specific death rates. In this study, we described a
mixture of polynomial time series (MoPTS) model to simultaneously identify (a) three clusters
of 86 U.S. cities in terms of their dynamic death rates, and (b) the different associations of
those rates with 5 key comorbidities among the populations in the clusters. We also described
an EM algorithm for efficient maximum likelihood estimation of the model parameters.
DOI：
10.1001/jama.2020.6775
发表时间：
2020-01-01
期刊：
JAMA, Journal of the American Medical Association
影响因子：
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作者：
Richardson, Safiya;Hirsch, Jamie S.;,
通讯作者：
,