Institute for Infection Prevention and Hospital Epidemiology, University Medical Center Freiburg, Medical Faculty, University of Freiburg, Freiburg, Germany.
Institute of Medical Biometry and Statistics, University Medical Center Freiburg, Medical Faculty, University of Freiburg, Freiburg, Germany.
Infect Control Hosp Epidemiol. 2021 Jun;42(6):653-658. doi: 10.1017/ice.2020.464. Epub 2020 Sep 15.
The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities.
We describe methods used by a university hospital to forecast case loads and time to peak incidence.
We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model).
The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data.
The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.
2019 年冠状病毒病（COVID-19）大流行带来的压力对医疗服务构成了前所未有的需求。当需要救生支持的患者超过可用能力时，医院很快就会不堪重负。
我们描述了一家大学医院用于预测病例数和发病高峰时间的方法。
我们开发了一组模型来预测医院集水区人群中的发病率，并描述 COVID-19 患者的医院护理途径。第一个预测利用了来自先前异地流行的数据，并根据专家意见对护理途径模型进行了参数化（即静态模型）。一旦获得了足够的本地数据，就拟合了时间相关有效繁殖数的趋势，并使用实际患者入院、转介和出院的风险对护理途径进行了重新参数化（即动态模型）。
在疫情爆发前部署的静态模型夸大了普通病房（预测 116 张，观察 66 张）、重症监护病房（预测 47 张，观察 34 张）的床位占用率，并预测发病高峰太晚：普通病房预测 4 月 9 日，观察 4 月 8 日，重症监护病房预测 4 月 19 日，观察 4 月 8 日。4 月 5 日之后，每天都可以运行动态模型，并且随着越来越多的本地经验数据可用，其精度得到了提高。
尽管服务需求被高估，但这些模型为大学医院在疫情激增之前的准备和关键资源分配提供了基于数据的指导。当可以考虑限制前后的人口接触模式时，估计过高的问题将得到解决，但目前，在不确定时期进行准备时，它们可能提供了一个可接受的安全边际。
