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：重症监护室占用率是评估医疗系统压力的重要指标，在COVID-19疫情期间被用于指导政策决策。为实现疫情期间的可靠决策，估算患者入院与出院速率（包括普通病房与重症监护室）至关重要。由于模型研究者通常难以获取各地区的个体层面医疗数据，开发基于公开每日医院及ICU占用床位数据推断这些速率的工具具有重要意义。我们提出了一种基于生灭过程的估算方法，用于模拟ICU占用率的波动。该灵活框架允许入迁率与消亡率依赖于协变量（如普通病房占用率、每日SARS-CoV-2检测阳性率等可能驱动ICU运营变化的因素）。通过模拟研究验证了该方法在含噪时间序列数据上的有效性，并将统计框架应用于加州大学尔湾分校健康中心及奥兰治县的住院数据。通过建立入迁率与消亡率可随协变量变化的似然框架，经严格模型选择发现，住院率与阳性率是建模ICU住院动态的关键协变量，并利用匿名化的UCI患者层面数据验证了每位患者ICU住院时长的估计结果。