Volume 54, March 2026, 100877Author links open overlay panel, , , , , , , Highlights•

Hospital electronic health records have potential for use in infectious disease forecasting.

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XGBoost outperforms neural networks in forecasting COVID-19 hospital admissions.

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Wastewater data improves forecasts of COVID-19 hospital admissions.

Abstract

During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmission dynamics and anticipate potential shortages of critical resources such as hospital capacity. In this study, we compared short-term forecasting approaches for COVID-19 hospital admissions that generate forecasts one to five weeks ahead, using retrospective electronic health records. We extracted different features (e.g., daily emergency department visits) from an individual-level patient dataset covering six hospitals located in the region of Bern, Switzerland, from February 2020 to June 2023. We then applied five methods – last-observation carried forward (baseline), linear regression, XGBoost and two types of neural networks – to time series using a leave-future-out training scheme with multiple cutting points and optimized hyperparameters. Performance was evaluated using the root mean square error between forecasts and observations. Generally, we found that XGBoost outperformed the other methods in predicting future hospital admissions. Our results also show that adding features such as the number of hospital admissions with fever and augmenting hospital data with measurements of viral concentration in wastewater improves forecast accuracy. This study offers a thorough and systematic comparison of methods applicable to routine hospital data for real-time epidemic forecasting. With the increasing availability and volume of electronic health records, improved forecasting methods will contribute to more precise and timely information during epidemic waves of COVID-19 and other respiratory viruses, thereby strengthening evidence-based public health decision-making.

Graphical abstractDownload: Download high-res image (170KB)Download: Download full-size imageKeywords

COVID-19

Hospital admissions

Forecasting

Local level

Machine learning

Electronic health records

Wastewater

Data and code availabilityAll code written in R and Python as well as some data and results files are publicly available in the GitHub repository ( github.com/mwohlfender/hospital_admission_forecasting). Due to data protection regulations we cannot make the full hospital dataset publicly available, but only in aggregated form.

© 2025 The Authors. Published by Elsevier B.V.