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

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 estima...

Full description

Saved in:

Bibliographic Details
Published in:	PloS one Vol. 16; no. 1; p. e0245272
Main Authors:	Goic, Marcel, Bozanic-Leal, Mirko S, Badal, Magdalena, Basso, Leonardo J
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 13-01-2021 Public Library of Science (PLoS)
Subjects:	Autoregressive models Biology and Life Sciences Central service department Civil engineering Computer and Information Sciences Computer programs Coronaviruses COVID-19 COVID-19 - epidemiology Critical care medicine Data analysis Decision analysis Decision making Draft (gas flow) Drafting software Editing Forecasting Forecasts and trends Hospital size Hospitals Humans Industrial engineering Intensive care units Intensive Care Units - statistics & numerical data Learning algorithms Machine learning Mechanical ventilation Medicine and Health Sciences Methodology Models, Statistical Neural Networks, Computer Pandemics People and places Physical Sciences Regions Research and Analysis Methods Software Statistics Technical information Ventilation Ventilators Visualization Chile
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0245272