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首页> 外文期刊>JMIR public health and surveillance. >Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence
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Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence

机译：Covid-19医院人口普查预测：基于局部感染率的多变量时间序列模型

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Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.
机译：背景Covid-19是世界历史上最严重的全球健康危机之一。在大流行期间，医疗保健系统需要准确的预测，以便指导患者飙升的准备。预测Covid-19医院人口普查是确保足够人员配置，床位，重症监护单位和重要设备的最重要规划决策之一。目的是本研究的目标是探讨当地Covid-19感染发病数据在开发Covid-19医院人口普查的预测模型方面的潜在效用。方法研究研究数据在北卡罗来纳州大夏洛特大都市地区的11个内核保健医院以及北卡罗来纳大都市地区的一家虚拟医院，以及在5月15日至12月至12月，在北卡罗来纳州的大夏洛特大都市地区的一家虚拟医院组成了综合的每日Covid-19医院人口普查数据5,2020，期间。计算了医院人口普查和局部感染发病率长达21天的互相关。用于传染料误差校正模型（VECM）的多变量时间序列框架用于同时合并时间序列并占他们可能的长期关系。对假设试验和模型诊断进行了测试，以测试长期关系，检查拟合模型的良好性。通过平均绝对百分比误差（MAPE）来测量7天的预测性能，随着时间序列交叉验证来衡量。还将预测性能与同一交叉验证时间帧中的自回归集成移动平均（ARIMA）模型进行了比较。基于大流行的不同情景，拟合的模型被利用以生产60天的预测。结果交叉相关性均匀高，落在0.7和0.8之间。有足够的证据表明，两次序列在.01意义水平上具有稳定的长期关系。该模型非常适合数据。样品外的MAPE中位数为5.9％，95百分位数为13.4％。相比之下，阿米马的Mape中位数为6.6％，95百分位数为14.3％。基于情景的60天前预测展示了凹陷的轨迹，山峰滞后2至3周后比峰值感染率。在最坏情况下，Covid-19医院人口普查可以达到比第二波期间观察到的峰值大的峰值超过3倍。结论在VECM框架中使用时，本地Covid-19感染发病率可以是预测Covid-19普查的有效领先指标。 VECM模型具有一个非常好的7天提前预测性能，表现优于传统的Arima模型。利用两次序列之间的关系，该模型可以生产现实的60天延伸的基于场景的预测，可以为医疗保健系统提供关于医院人口普查的高峰时序和体积的长期规划目的。

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