Castro Lauren A, Shelley Courtney D, Osthus Dave, Michaud Isaac, Mitchell Jason, Manore Carrie A, Del Valle Sara Y
Information Systems & Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States.
Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, United States.
JMIR Public Health Surveill. 2021 Jun 9;7(6):e27888. doi: 10.2196/27888.
Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe.
Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions.
We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory's COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground.
Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs.
Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.
在新冠疫情大流行之前，美国医院依靠对未来趋势的静态预测进行长期规划，并且才刚刚开始考虑用于人员配置和其他资源短期规划的预测方法。随着新冠疫情给医疗系统带来的巨大负担，迫切需要在可操作的时间范围内准确预测住院需求。
我们的目标是利用现有的新冠病例和死亡预测工具，生成新墨西哥州及其五个卫生区域未来1天至4周内同时住院的预期人数、占用的重症监护病房（ICU）床位数量以及正在使用的呼吸机数量。
我们开发了一个概率模型，该模型将洛斯阿拉莫斯国家实验室使用快速评估和估计工具进行的新冠疫情预测中提供的新墨西哥州新冠新病例数作为输入，并根据当前全州的住院率，使用该模型估计未来4周每天新的住院人数。该模型估计需要ICU床位或使用呼吸机的新入院人数，然后根据资源需求预测个体住院时长。通过跟踪住院时长随时间的变化，我们得出了对住院床位、ICU床位和呼吸机的预计同时需求。我们使用一种后处理方法，根据先前预测与后续观察数据之间的差异来调整预测。因此，我们确保了我们的预测能够反映实际情况的动态变化。
2020年9月1日至12月9日期间做出的预测在不同时间、医疗资源需求和预测期内显示出不同的准确性。10月份做出的预测，当时新冠新病例稳步增加，平均准确率误差为20.0%，而9月份做出的预测误差为39.7%，9月份是新冠活动较低的一个月。在各类医疗用途中，州级预测比地区级预测更准确。尽管随着预测期延长到更远的未来，准确性有所下降，但预测的既定不确定性有所改善。对于州级住院和ICU需求，在3至4周的预测期内，预测值在其50%和90%预测区间的既定不确定性的5%以内。然而，对于州级呼吸机需求和所有地区医疗资源需求的预测，不确定性区间过窄。
对传染病传播造成的负担进行实时预测是公共卫生紧急事件期间决策支持的关键组成部分。我们提出的方法在提供短期预测方面显示出实用性，特别是在州级层面。这个工具可以帮助其他利益相关者应对他们现在和未来面临的新冠疫情对人群的影响。
