随着COVID-19病毒开始控制美国, 这种病毒传播的速度有多快还不确定, 这些病例可能有多严重, 以及高传染性患者将如何影响医院病床的使用, 通风, 人员配备, 和个人防护用品. 对于医疗保健组织, 这种情况使人们越来越关注行动规程, 特别是关于需要激增容量来满足高峰床的需求.
这些担忧并没有减轻——covid - 19阳性病例正在减轻, 事实上, 全国范围内不断增加, 增加了流感季节的年度需求, 扩展容量限制, 并带来运营挑战和财政压力.
南加州大学的凯克医学, 位于洛杉矶的619个床位的学术医疗系统, needed help determining how the first wave of COVID-19 patients might affect its hospitals and reached out to 心电图 to develop a forecasting model. 与凯克领导一起工作, 心电图 forecasted the spread of COVID-19 throughout Los Angeles County and tracked aggregate bed demand in the area and for Keck’s two general acute hospitals—Keck Hospital of USC (KH) and USC Verdugo Hills Hospital (VHH). This modeling helped Keck plan for both COVID-19-positive and COVID-19-negative patients within its intensive care, 外科, 还有儿科病房. 重要的是, it also helped Keck determine when and how to ramp up nonessential procedures following the statewide elective procedure postponement.
I. 对病毒的预期影响进行建模
由于新型病毒的性质以及缺乏对COVID-19的文献研究, it was unclear which 传染性 disease model would most appropriately capture the virus’s behavior and enable adequate prediction. 心电图选用易感者, 传染性, 和恢复(SIR)模型, 一个证明, 微分方程模型,将总体分为三个部分- s, I, r表示一组随时间变化的因变量. One of the benefits of the SIR model is that it required fewer assumptions about uncertain aspects of the virus, such as whether asymptomatic patients were contagious and how long patients would remain immune after recovering.
心电图 and Keck worked with the Los Angeles County Department of Public Health as a source for daily infection counts over a four-month period, 利用 可用的数据 按年龄分组排列. The model was built and populated with this data along with the most recently available census data.
典型的疾病传播模型, the input assumptions are fairly well tested and studied; but due to the nature of the novel coronavirus, 该模型的其他输入需要更广泛的假设, including the average contagious period; the R0, 或者传输速率, which varies and changes based on a population’s behavior; and the comprehensiveness of testing. 该模型的结构使其产生保守性, 温和的, 和激进的场景,以提供一系列的输出. Data trends were captured so the forecast could easily be updated with historical information.
Figure 1 is one of the graphs created to illustrate the spread of COVID-19 throughout Los Angeles County.
Note: New infections per day adjustment assumes that only 35% of new cases are being reported, a variable built into the model that was modified as time passed based on a variety of factors.
II. 床上的需求
确定全县的总体床位需求, we modeled both the total number of available beds county-wide and the number of non-COVID-19-related beds needed. 作为这些总数的基线, we employed California’s Office of Statewide Health Planning and Development (OSHPD) utilization and financial data for all Los Angeles County general acute care hospitals. 尽管这一数据滞后于当前情况, 它被证明是最适用和最容易获得的.
Compounding what the data showed was the effect COVID-19 was having on non-COVID-related patient volumes. With elective procedures being canceled and patients steering clear of emergency departments (EDs), 医院的基准病床使用率正在下降. Assumptions were developed about the degree of volume decrease expected county-wide then tracked and regularly modified and updated against Keck’s actual experience. The gap between the decreased baseline demand and the number of available beds represented the capacity available for COVID-19-positive patients.
图2显示了洛杉矶县的床位需求. It indicates a lull in bed demand in May due to decreased ED visits and elective procedures and then demand increasing again beginning in June as the virus continued to spread.
With models in place for the predicted bed availability and number of individuals 受感染的 with COVID-19 on any given day, 感染人数可能会转化为住院治疗. 这就是年龄队列数据能够提供更精确预测的地方, 因为老年人比年轻人更容易感染这种病毒. 相应的, 住院率是假设的, 包括需要加护病房床位的住院比例, 基于年龄组敏感性和历史追踪数据, 随着时间的推移进行调整. 我们还对住院时间做了假设, 对每个年龄群和相应的床型敏感, to understand how the inpatient COVID-19 volumes might compound as the days and weeks progressed.
Figure 3 illustrates anticipated KH ICU bed demand based on the hospital’s non-COVID-19-related ICU population and the impact on KH of the virus’s spread. 尽管KH没有ED, which was the “front door” for COVID-19 patients at so many other healthcare provider organizations, 观察到COVID-19床位需求,并通过患者转移进行仔细管理.
3. 结论和扩展可能性
该模型从2020年4月到8月初每天更新和监测, when the spread of the virus slowed enough to ease operational concerns regarding bed capacity by type—at least as of this writing. 幸运的是, the bed demand resulting from COVID-19 did not exceed the bed supply in Los Angeles County through October 2020, 也不是在KH或VHH. Each hospital established COVID-19 bed units but did not need to create new or temporary beds to meet demand. 这种情况正在改变, 然而, 由于预期的病例激增已经实现, 50个州中有41个州的阳性病例和住院病例不断增加
最终, insight gained through the modeling exercise—fueled by vigilant maintenance of the model—allowed Keck to responsibly return to a higher occupancy rate, 更有信心, 在一个加速的时间框架内. Keck was able to systematically ramp up elective procedures to help mitigate the financial strain it experienced during the peak of the pandemic (common to most hospitals during that time). 是否会遇到第二波COVID-19或另一场大流行, this model can be revived for Keck or similarly adapted for any other hospital or health system across the country.
在本文发表时, a vaccine is not publicly available and the current understanding is that individuals who are 受感染的 can be re受感染的 months later. The possibility that COVID-19 immunity wanes over a period of several months may indicate that a SIRS (i.e., 易受影响, 受感染的, 恢复, and 易受影响) model could be a more accurate prediction tool over an extended period of time.
随着COVID-19病例的增加和流感季节的到来, contact 心电图 to learn how we can help your organization better forecast bed need and resource deployment.
bet8娱乐2020年11月4日发布