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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
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.
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.
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