Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK.
During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022-23 we pil...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0292829 |
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| _version_ | 1832540171690573824 |
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| author | Roger A Morbey Dan Todkill Phil Moura Liam Tollinton Andre Charlett Conall Watson Alex J Elliot |
| author_facet | Roger A Morbey Dan Todkill Phil Moura Liam Tollinton Andre Charlett Conall Watson Alex J Elliot |
| author_sort | Roger A Morbey |
| collection | DOAJ |
| description | During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022-23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. Forecast uncertainty was modelled usings a novel generalised additive model for location, scale and shape (gamlss) approach which enabled prediction intervals to vary according to the level of the forecast activity. The winter of 2022-23 was atypical because the demand for healthcare services in children was exceptionally high, due to RSV circulating in the community and increased concerns around invasive group A streptococcal (iGAS) infections. However, our short-term forecasts proved to be adaptive forecasting a new higher peak once the increasing demand due to iGAS started. Thus, we have demonstrated the utility of our approach, adding forecasts to existing surveillance systems. |
| format | Article |
| id | doaj-art-3b71c2849b3c414a912d80ce8989c838 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3b71c2849b3c414a912d80ce8989c8382025-02-05T05:32:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e029282910.1371/journal.pone.0292829Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK.Roger A MorbeyDan TodkillPhil MouraLiam TollintonAndre CharlettConall WatsonAlex J ElliotDuring winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022-23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. Forecast uncertainty was modelled usings a novel generalised additive model for location, scale and shape (gamlss) approach which enabled prediction intervals to vary according to the level of the forecast activity. The winter of 2022-23 was atypical because the demand for healthcare services in children was exceptionally high, due to RSV circulating in the community and increased concerns around invasive group A streptococcal (iGAS) infections. However, our short-term forecasts proved to be adaptive forecasting a new higher peak once the increasing demand due to iGAS started. Thus, we have demonstrated the utility of our approach, adding forecasts to existing surveillance systems.https://doi.org/10.1371/journal.pone.0292829 |
| spellingShingle | Roger A Morbey Dan Todkill Phil Moura Liam Tollinton Andre Charlett Conall Watson Alex J Elliot Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. PLoS ONE |
| title | Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. |
| title_full | Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. |
| title_fullStr | Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. |
| title_full_unstemmed | Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. |
| title_short | Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. |
| title_sort | using machine learning to forecast peak health care service demand in real time during the 2022 23 winter season a pilot in england uk |
| url | https://doi.org/10.1371/journal.pone.0292829 |
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