Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic
新冠疫情期间利用含噪住院时间序列拟合 ICU 占床随机模型AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确以每日住院与 ICU 占床时间序列拟合随机动态模型，分类依据是时间序列方法而非应用属于宏观经济。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
新冠疫情期间利用含噪住院时间序列拟合 ICU 占床随机模型
- 研究问题
- 缺少个体住院记录时，如何从每日医院与 ICU 占床数推断患者流入和退出速率，并解释 ICU 占床动态的变化？
- 方法与思路
- 作者用移入—消亡过程刻画 ICU 占床波动，在似然框架中允许速率依赖医院占床与检测阳性率，并开展模拟、模型选择和数据验证。
- 材料陈述的贡献
- 摘要称模型能处理含噪占床序列，应用识别出住院与阳性率的重要性，并以匿名个体住院数据验证人均 ICU 停留时间估计。
- 阅读关注点
- 应核对占床总量对流入和退出率的识别、协变量作用形式、噪声处理与模型选择，以及个体记录验证的具体指标。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要未说明参数识别条件或验证误差，协变量重要性不能直接解释为因果作用。
原文摘要
Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.