COVID-19: Short-term forecast of ICU beds in times of crisis

被引:41
作者
Goic, Marcel [1 ]
Bozanic-Leal, Mirko S. [1 ,2 ]
Badal, Magdalena [1 ,2 ]
Basso, Leonardo J. [2 ,3 ]
机构
[1] Univ Chile, Dept Ind Engn, Santiago, Chile
[2] Inst Sistemas Complejos Ingn ISCI, Santiago, Chile
[3] Univ Chile, Dept Civil Engn, Santiago, Chile
来源
PLOS ONE | 2021年 / 16卷 / 01期
关键词
SERIES; MODELS; PREDICTION; EPIDEMIC; PACKAGE; SPREAD;
D O I
10.1371/journal.pone.0245272
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
By early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models.
引用
收藏
页数:24
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