Forecasting regional COVID‐19 regional COVID-19 hospitalisation in England using ordinal machine learning method
File(s)
Published version
Author(s)
Riley, Steven
Wang, Haowei
Wang, Haowei
Type
Journal Article
Abstract
Background
The COVID-19 pandemic caused substantial pressure on healthcare, with many systems needing to prepare for and mitigate the consequences of surges in demand caused by multiple overlapping waves of infections. Therefore, public health agencies and health system managers also benefitted from short-term forecasts for respiratory infections that allowed them to manage services. While quantitative forecasts treating hospital admissions as continuous variables existed, many health managers prefer discrete levels of demand, similar to approaches used in weather and flooding. However, effective tools for generating precise sub-national forecasts remained limited.
Methods
We forecast regional COVID-19 hospitalisations in England, using the period from March 2020 to December 2021 for training and evaluating predictions using data from January to December 2022. We transform regional admission counts into an ordinal variable using n-tile and n-uniform methods. We further developed a method based on XGBoost, and used previously for influenza, to enable it to exploit the ordering information in ordinal hospital admission levels. We incorporated different types of data as predictors: epidemiological data including weekly region COVID-19 cases and hospital admissions, weather conditions and mobility data for multiple categories of locations. The impact of different discretisation methods and the number of ordinal levels was also considered.
Results
We found that mobility data brings about a more substantial improvement in predictive performance than relying only on epidemiological data and the inclusion of weather data. When both weather and mobility data are used in addition to epidemiological data, the results are very similar to models with only epidemiological data and mobility data. These results are robust in terms of the number of levels chosen for the forecast target.
Conclusion
Accurate ordinal forecasts of COVID-19 hospitalisations were obtained using XGBoost and mobility data. While uniform ordinal levels showed higher apparent accuracy, we recommend n-tile ordinal levels which contain far richer information.
The COVID-19 pandemic caused substantial pressure on healthcare, with many systems needing to prepare for and mitigate the consequences of surges in demand caused by multiple overlapping waves of infections. Therefore, public health agencies and health system managers also benefitted from short-term forecasts for respiratory infections that allowed them to manage services. While quantitative forecasts treating hospital admissions as continuous variables existed, many health managers prefer discrete levels of demand, similar to approaches used in weather and flooding. However, effective tools for generating precise sub-national forecasts remained limited.
Methods
We forecast regional COVID-19 hospitalisations in England, using the period from March 2020 to December 2021 for training and evaluating predictions using data from January to December 2022. We transform regional admission counts into an ordinal variable using n-tile and n-uniform methods. We further developed a method based on XGBoost, and used previously for influenza, to enable it to exploit the ordering information in ordinal hospital admission levels. We incorporated different types of data as predictors: epidemiological data including weekly region COVID-19 cases and hospital admissions, weather conditions and mobility data for multiple categories of locations. The impact of different discretisation methods and the number of ordinal levels was also considered.
Results
We found that mobility data brings about a more substantial improvement in predictive performance than relying only on epidemiological data and the inclusion of weather data. When both weather and mobility data are used in addition to epidemiological data, the results are very similar to models with only epidemiological data and mobility data. These results are robust in terms of the number of levels chosen for the forecast target.
Conclusion
Accurate ordinal forecasts of COVID-19 hospitalisations were obtained using XGBoost and mobility data. While uniform ordinal levels showed higher apparent accuracy, we recommend n-tile ordinal levels which contain far richer information.
Date Issued
2025-12-01
Date Acceptance
2025-09-08
Citation
Epidemics, 2025, 53
ISSN
1755-4365
Publisher
Elsevier
Journal / Book Title
Epidemics
Volume
53
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41027240
PII: S1755-4365(25)00044-1
Subjects
Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
SARS-CoV-2
COVID-19
Machine Learning
Infectious Disease Forecasting
HUMAN MOBILITY
Publication Status
Published
Coverage Spatial
Netherlands
Article Number
100856
Date Publish Online
2025-09-11