Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, NC, United States.
JMIR Public Health Surveill. 2021 Aug 4;7(8):e28195. doi: 10.2196/28195.
COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment.
The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census.
The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts.
The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave.
When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.
COVID-19 是世界历史上最严重的全球卫生危机之一。在大流行期间，医疗保健系统需要对关键资源进行准确预测，以指导应对患者激增的准备工作。预测 COVID-19 医院普查是确保人员配备、床位数量、重症监护病房和重要设备充足的最重要的规划决策之一。
本研究旨在探讨当地 COVID-19 感染发病率数据在开发 COVID-19 医院普查预测模型中的潜在应用。
研究数据包括北卡罗来纳州夏洛特大都市区 11 家 Atrium Health 医院和一家虚拟医院的 COVID-19 医院普查的汇总日数据，以及同期整个地区的总日感染发病率。计算了医院普查和当地感染发病率之间最长可达 21 天的滞后交叉相关。使用称为向量误差校正模型（VECM）的多变量时间序列框架来同时纳入两个时间序列，并考虑它们可能的长期关系。进行了假设检验和模型诊断，以检验长期关系并检查模型拟合优度。通过时间序列交叉验证，以平均绝对百分比误差（MAPE）衡量 7 天的预测性能。还将预测性能与同一交叉验证时间框架内的自回归综合移动平均（ARIMA）模型进行了比较。基于大流行的不同情况，利用拟合模型生成 60 天的预测。
交叉相关系数均很高，介于 0.7 和 0.8 之间。有足够的证据表明，两个时间序列在.01 的显著水平上具有稳定的长期关系。该模型非常适合数据。样本外 MAPE 的中位数为 5.9%，第 95 百分位数为 13.4%。相比之下，ARIMA 的 MAPE 中位数为 6.6%，第 95 百分位数为 14.3%。基于情景的 60 天预测显示出凹形轨迹，峰值滞后于感染发病率峰值 2 至 3 周。在最坏的情况下，COVID-19 医院普查的峰值可能超过第二波观察到的峰值的 3 倍以上。
当地 COVID-19 感染发病率在 VECM 框架中使用时，可以作为预测 COVID-19 医院普查的有效领先指标。VECM 模型具有非常好的 7 天预测性能，优于传统的 ARIMA 模型。利用两个时间序列之间的关系，该模型可以生成现实的 60 天基于情景的预测，为长期规划目的告知医疗保健系统医院普查的峰值时间和数量。
