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Updated: Sep 11, 2025

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Evaluating short-term forecast accuracy across COVID-19 waves using penalized spline models.

Nere Larrea1, Dae-Jin Lee2, Irantzu Barrio3

  • 1Research Unit, Galdakao-Usansolo University Hospital, Galdakao, Spain; Biosistemak Institute for Health Systems Research, Barakaldo, Spain; Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Spain.

International Journal of Medical Informatics
|August 13, 2025
PubMed
Summary

COVID-19 forecasting models accurately predicted ICU admissions but struggled with hospitalizations and case counts, especially during the Omicron wave. Reliable health data systems are crucial for effective epidemic prediction.

Keywords:
COVID-19PandemicRMSEShort-term forecastingSmoothing methods

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Area of Science:

  • Epidemiology
  • Health Informatics
  • Biostatistics

Background:

  • Daily monitoring of COVID-19 evolution was essential during the pandemic.
  • This study assesses the accuracy of short-term epidemic forecasting models.

Purpose of the Study:

  • To evaluate the effectiveness of a specific modeling approach for short-term COVID-19 forecasts.
  • To determine if the models could accurately identify different epidemic phases.

Main Methods:

  • Utilized penalized regression splines and Negative Binomial distribution for daily SARS-CoV-2 cases, hospitalizations, and ICU admissions.
  • Applied a generalized additive model with penalties for 2- and 5-day predictions.
  • Evaluated prediction errors using root median square error and relative error.

Main Results:

  • Models m1 and m2 showed high accuracy (70-80%) during quarantine and state of emergency.
  • Prediction accuracy decreased during the Omicron wave, with 5-day case predictions ranging from 50-74%.
  • Peak hospital admissions reached 314, and ICU admissions peaked at 39.

Conclusions:

  • Models performed well for ICU admissions but less so for hospital admissions and case counts.
  • Forecasting accuracy diminished during periods of rapid trend changes, such as the Omicron wave.
  • Emphasizes the need for robust health information systems for reliable daily data to support clinical and managerial decision-making.