Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
Epidemics. 2022 Jun;39:100580. doi: 10.1016/j.epidem.2022.100580. Epub 2022 May 23.
During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose. We evaluate its historical performance using metrics such as mean absolute error, predictive interval coverage, and weighted interval scores, and compare to alternative hospital admission forecasting models. We find that Gecko outperformed baseline approaches and was among the most accurate models for forecasting hospital admissions at the state and national levels from January-May 2021. This work suggests that simple statistical methods can provide a viable alternative to traditional epidemic models for short-term forecasting.
在 COVID-19 大流行期间，美国对医院容量的担忧导致了对预测 COVID-19 住院人数的模型的需求。这些短期预测是必要的，它可以为决策者提供对未来医疗能力和资源需求的洞察力，以支持规划工作。我们提出了一种名为 Gecko 的 SARIMA 时间序列模型，用于实现这一目标。我们使用平均绝对误差、预测区间覆盖率和加权区间评分等指标来评估其历史表现，并与其他医院入院预测模型进行比较。我们发现，Gecko 的表现优于基准方法，并且是 2021 年 1 月至 5 月在州和国家层面预测医院入院人数最准确的模型之一。这项工作表明，简单的统计方法可以为短期预测提供一种可行的替代传统流行模型的方法。
