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

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  • Marcel Goic
  • Mirko S Bozanic-Leal
  • Magdalena Badal
  • Leonardo J Basso

Abstract

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.

Suggested Citation

  • Marcel Goic & Mirko S Bozanic-Leal & Magdalena Badal & Leonardo J Basso, 2021. "COVID-19: Short-term forecast of ICU beds in times of crisis," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-24, January.
    • Handle: RePEc:plo:pone00:0245272
    DOI: 10.1371/journal.pone.0245272
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    References listed on IDEAS

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    1. Ward, Michael J. & Marsolo, Keith A. & Froehle, Craig M., 2014. "Applications of business analytics in healthcare," Business Horizons, Elsevier, vol. 57(5), pages 571-582.
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    Cited by:

    1. Navarro-García, Manuel & Guerrero, Vanesa & Durban, María, 2023. "On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    2. Bekker, René & uit het Broek, Michiel & Koole, Ger, 2023. "Modeling COVID-19 hospital admissions and occupancy in the Netherlands," European Journal of Operational Research, Elsevier, vol. 304(1), pages 207-218.
    3. Aldo Carranza & Marcel Goic & Eduardo Lara & Marcelo Olivares & Gabriel Y. Weintraub & Julio Covarrubia & Cristian Escobedo & Natalia Jara & Leonardo J. Basso, 2022. "The Social Divide of Social Distancing: Shelter-in-Place Behavior in Santiago During the Covid-19 Pandemic," Management Science, INFORMS, vol. 68(3), pages 2016-2027, March.
    4. Leonardo J. Basso & Marcel Goic & Marcelo Olivares & Denis Sauré & Charles Thraves & Aldo Carranza & Gabriel Y. Weintraub & Julio Covarrubia & Cristian Escobedo & Natalia Jara & Antonio Moreno & Demia, 2023. "Analytics Saves Lives During the COVID-19 Crisis in Chile," Interfaces, INFORMS, vol. 53(1), pages 9-31, January.
    5. Maria Victoria Ibañez & Marina Martínez-Garcia & Amelia Simó, 2021. "A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data," Mathematics, MDPI, vol. 9(13), pages 1-23, July.
    6. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    7. Michael R. Johnson & Hiten Naik & Wei Siang Chan & Jesse Greiner & Matt Michaleski & Dong Liu & Bruno Silvestre & Ian P. McCarthy, 2023. "Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions," Health Care Management Science, Springer, vol. 26(3), pages 477-500, September.
    8. Dijkstra, Sander & Baas, Stef & Braaksma, Aleida & Boucherie, Richard J., 2023. "Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy," Omega, Elsevier, vol. 116(C).

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