University of Pennsylvania, Philadelphia, Pennsylvania (G.E.W., M.Z.L., G.L.A., P.J.B., J.D.C., C.W.H., M.E.M., S.D.H.).
University of Pennsylvania and Penn Medicine Predictive Healthcare, Philadelphia, Pennsylvania (A.C., M.E.D.).
Ann Intern Med. 2020 Jul 7;173(1):21-28. doi: 10.7326/M20-1260. Epub 2020 Apr 7.
The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.
To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.
Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.
3 hospitals in an academic health system.
All people living in the greater Philadelphia region.
The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.
Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.
Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction.
Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.
University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.
2019 年冠状病毒病（COVID-19）大流行挑战着医院领导，使他们能够就临床运营和资源分配做出及时、关键的决策。
估计临床需求的激增时间，并对当地 COVID-19 引发的医院容量紧张的最佳和最坏情况进行评估，从而为临床运营和人员配备需求提供信息，并确定医院容量何时会饱和。
易感者、感染者、清除者（SIR）模型的蒙特卡罗模拟实例，周期为 1 天。
学术医疗系统中的 3 家医院。
居住在大费城地区的所有人。
COVID-19 医院影响模型（CHIME）（http://penn-chime.phl.io）SIR 模型用于估计从 2020 年 3 月 23 日起至医院容量可能超过的时间，以及包括重症监护病房（ICU）床位和呼吸机在内的激增强度。
仅使用 COVID-19 患者，CHIME 估计需要 31 到 53 天的时间才能超过现有医院容量。在 COVID-19 患者人数激增的最佳和最坏情况下，3 家医院总共需要 3131 到 12650 张床位，包括 338 到 1608 张 ICU 床位和 118 到 599 台呼吸机。
模型参数直接取自或源自异质人群和实践环境中的已发表数据以及该医疗系统的历史数据。CHIME 未纳入更多的感染状态来模拟感染严重程度，也未纳入社交网络来模拟传播动力学，或地理信息来解释人类互动的空间模式。
这款可供公众使用并专为医院运营领导者设计的建模工具可以为大流行早期的容量压力做准备。
宾夕法尼亚大学卫生系统和姑息治疗与晚期疾病研究中心。
