模拟德克萨斯州的COVID-19阳性率和住院率

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2021-01-01 DOI:10.3233/MAS-210514

R. Kafle, Dooyoung Kim, Martin E. Malandro, M. Holt

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引用次数: 4

摘要

本研究的目的是利用贝叶斯联点回归对德克萨斯州COVID-19检测阳性率和住院率进行联合建模。检测阳性率和住院率的数据是在2020年4月5日至10月19日期间从德克萨斯州卫生服务部获得的。第一阶段模型确定了检测阳性率的四个重大变化，其中三个发生在全州范围内记录的政策或行为变化后大约9天。然后使用第一个模型估计的阳性率来预测住院率，并估计阳性率变化与住院之间的滞后时间。产生的滞后时间为9.056天(±3.808)。这两种模型对政策制定者和公共卫生官员都很有价值，因为他们研究行为模式对疾病流行和由此导致的住院治疗的影响。©2021 - IOS出版社。版权所有。

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Modeling COVID-19 positivity rates and hospitalizations in Texas

The aim of this study was to jointly model COVID-19 test positivity rates and hospitalizations in Texas using Bayesian joinpoint regression. The data for both test positivity rates and hospitalizations were obtained from the Texas Department of State Health Services between April 5 and October 19, 2020. The stage 1 model identifies four significant shifts in test positivity rates, three of which occur roughly 9 days after documented policy or behavioral changes statewide. Estimated positivity rates from the first model were then used to predict hospitalization rates and to estimate lag time between changes in positivity and hospitalization. The resulting lag time is 9.056 days (± 3.808). Both models are valuable to policy makers and public health officials as they study the impact of behavioral patterns on disease prevalence and resulting hospitalizations. © 2021 - IOS Press. All rights reserved.

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来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.