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Predicting use of intensive care units during the COVID-19 pandemic

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Abstract

With the prevalence of the SARS-CoV-2 pandemic, sudden planning needs emerged in intensive care units (ICUs) in many countries, particularly Chile. Chile was chosen for this study due to its diverse geographical regions, which presented unique challenges in managing ICU capacity during the pandemic. The researchers’ understanding of the local healthcare system provided a significant advantage in accurately analyzing these challenges. In ICUs, the most severe COVID-19 patients require specialized treatment, stressing operational-level decision-making. Understanding patient arrival dynamics became essential to predicting the additional ICU beds needed. We propose ten approaches using machine learning and classical time series models to estimate the required beds, setting upper and lower bounds. Evaluating the predictions with 2020 and 2021 data from three representative regions produced lower errors in the largest region. The low errors produced by the Holt-Winters model suggest that the data have seasonal and trend characteristics. Specifically, Holt-Winters achieved a mean absolute error of 0.00 in the smallest region and 9.26 in the largest region, demonstrating its effectiveness in forecasting ICU demand. Although the models were evaluated in only three regions, extending them to other situations would require training with local data.

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Data Availability

The raw data used in this study is freely available through the references Innovación 2021 and Observa. COVID-19 DATA Data 2023. The processed data supporting the findings of this article will be shared upon reasonable request to the corresponding author.

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Funding

This research was funded by CONICYT PIA/BASAL—AFB180003 and Fulbright Scholar in Residence Program—PS00307513.

Author information

Authors and Affiliations

  1. Departamento de Ingeniería Industrial, Universidad de Concepción, Edmundo Larenas 215, Concepción, 4070043, Chile

    Kathyana Perez, José M. Slater & Lorena Pradenas

  2. Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Instituto Sistemas Complejos de Ingeniería (ISCI), Av. Ecuador 3659, Santiago, Chile

    Victor Parada

  3. Michael Neidorff School of Business, Trinity University, One Trinity Place, San Antonio, TX, 78212-7200, USA

    Robert F. Scherer

Corresponding author

Correspondence to Robert F. Scherer.

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Conflicts of interest

The authors have no conflicts of interest with respect to this research/study.

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Cite this article

Perez, K., Slater, J.M., Pradenas, L. et al. Predicting use of intensive care units during the COVID-19 pandemic. Oper Manag Res (2025). https://doi.org/10.1007/s12063-025-00558-9

  • Received
  • Revised
  • Accepted
  • Published
  • DOI https://doi.org/10.1007/s12063-025-00558-9

Keywords

  • COVID-19
  • ICU prediction
  • Machine learning
  • Time series forecasting
  • Healthcare operations management
  • Regression
  • Intensive care units

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