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.

翻译：重症监护室（ICU）占用率是衡量医疗系统压力的重要指标，已被用于指导COVID-19疫情期间的政策决策。为实现疫情发展过程中的可靠决策，估算患者入院和出院（含ICU）的速率至关重要。由于研究者通常难以获取各目标地区的个体层面医院数据，开发基于公开的每日医院和ICU床位占用数据推断这些速率的方法具有重要价值。本文基于生灭过程构建了一种估算方法，用于模拟ICU占用率的波动。该灵活框架允许迁入率和迁出率依赖于协变量（如医院床位占用率和每日SARS-CoV-2检测阳性率），这些因素可能驱动医院ICU运营变化。通过模拟研究表明，所提方法对含噪声的时间序列数据具有良好表现，并将统计框架应用于加州大学欧文分校（UCI）卫生系统及橙县的住院数据。通过引入允许迁入率和迁出率随协变量变化的似然框架，经严格模型选择发现，住院率和阳性率是建模ICU滞留动态的关键协变量，并利用匿名化的UCI医院患者级数据验证了每位患者的ICU滞留估算结果。