How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.
How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.
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DOI：
10.2196/27888
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发表时间：
2021-06-09
影响因子：
8.5
通讯作者：
Del Valle SY
中科院分区：
文献类型：
--
作者：
Castro LA;Shelley CD;Osthus D;Michaud I;Mitchell J;Manore CA;Del Valle SY
在COVID-19大流行之前，美国医院依赖对未来趋势的静态预测进行长期规划，并且仅开始考虑预测方法用于人员配置和其他资源的短期规划。随着COVID-19对医疗保健系统造成的巨大负担，迫切需要在可行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COVID-19病例和死亡预测工具，为新墨西哥州及其五个卫生区域生成未来1天至4周的预期并发住院人数、重症监护病房（ICU）床位占用人数和使用中的呼吸机数量。我们开发了一个概率模型，将来自洛斯阿拉莫斯国家实验室的COVID-19预测使用快速评估和估计工具的新墨西哥州新的COVID-19病例数作为输入，我们使用该模型根据当前全州的住院率估计未来4周的每日新入院人数。该模型估计了需要ICU病床或使用呼吸机的新入院人数，然后根据资源需求预测了个人住院时间。通过跟踪住院时间的长短，我们获得了住院床位、ICU床位和呼吸机的预计同时需求。我们使用后处理方法来调整预测的基础上先前的预测和随后的观测数据之间的差异。因此，我们确保了我们的预测能够反映当地不断变化的局势。在2020年9月1日至12月9日期间进行的预测显示，随着时间的推移，医疗保健资源需求和预测范围的变化，准确性会有所不同。10月份新冠肺炎病例稳步增加，预测的平均准确性误差为20.0%，而9月份新冠肺炎活动较低，预测的平均准确性误差为39.7%。在医疗保健使用类别中，州一级的预测比地区一级的预测更准确。虽然准确性随着预测的进一步发展而下降，但预测的不确定性有所改善。在州一级住院病人和ICU需求的3至4周预测范围内，在50%和90%预测区间，预测结果在其声明的不确定性的5%以内。然而，不确定性区间太窄，预测国家一级的呼吸机需求和所有区域的医疗保健资源需求。实时预测传染病传播造成的负担是突发公共卫生事件决策支持的重要组成部分。我们提出的方法在提供短期预测方面表现出实用性，特别是在州一级。该工具可以帮助其他利益相关者，因为他们现在和未来都面临COVID-19的人口影响。
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.
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影响因子：
13.8
作者：
McCoy Jr, Thomas H.;Pellegrini, Amelia M.;Perlis, Roy H.
通讯作者：
Perlis, Roy H.
影响因子：
3.3
作者：
Bouckaert, Nicolas;Van den Heede, Koen;Van de Voorde, Carine
通讯作者：
Van de Voorde, Carine
影响因子：
3.6
作者：
Mackay, Mark;Lee, Michael
通讯作者：
Lee, Michael
DOI：
10.1073/pnas.2004064117
发表时间：
2020-04-21
影响因子：
11.1
作者：
Moghadas, Seyed M.;Shoukat, Affan;Galvani, Alison P.
通讯作者：
Galvani, Alison P.
DOI：
10.1093/jamia/ocaa140
发表时间：
2020-12-01
影响因子：
6.4
作者：
Jiao, York;Sharma, Anshuman;Kannampallil, Thomas
通讯作者：
Kannampallil, Thomas