COVID-19: Short-Term Forecast of ICU Beds in Times of Crisis

By early May 2020, the number of new infections of COVID-19 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 to 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 give relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all 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 a one and two-week horizon respectively, outperforming several other competing forecasting models.