Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms.

Autor: Wang X; School of Public Health, Nanjing Medical University, Nanjing, China.; Centre for Global Health, The University of Edinburgh, Edinburgh, UK., Dong Y; Edinburgh Medical School, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK., Thompson WD; Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK., Nair H; Centre for Global Health, The University of Edinburgh, Edinburgh, UK., Li Y; School of Public Health, Nanjing Medical University, Nanjing, China.; Centre for Global Health, The University of Edinburgh, Edinburgh, UK.
Jazyk: angličtina
Zdroj: Communications medicine [Commun Med (Lond)] 2022 Sep 24; Vol. 2, pp. 119. Date of Electronic Publication: 2022 Sep 24 (Print Publication: 2022).
DOI: 10.1038/s43856-022-00184-7
Abstrakt: Background: Short-term prediction of COVID-19 epidemics is crucial to decision making. We aimed to develop supervised machine-learning algorithms on multiple digital metrics including symptom search trends, population mobility, and vaccination coverage to predict local-level COVID-19 growth rates in the UK.
Methods: Using dynamic supervised machine-learning algorithms based on log-linear regression, we explored optimal models for 1-week, 2-week, and 3-week ahead prediction of COVID-19 growth rate at lower tier local authority level over time. Model performance was assessed by calculating mean squared error (MSE) of prospective prediction, and naïve model and fixed-predictors model were used as reference models. We assessed real-time model performance for eight five-weeks-apart checkpoints between 1st March and 14th November 2021. We developed an online application (COVIDPredLTLA) that visualised the real-time predictions for the present week, and the next one and two weeks.
Results: Here we show that the median MSEs of the optimal models for 1-week, 2-week, and 3-week ahead prediction are 0.12 (IQR: 0.08-0.22), 0.29 (0.19-0.38), and 0.37 (0.25-0.47), respectively. Compared with naïve models, the optimal models maintain increased accuracy (reducing MSE by a range of 21-35%), including May-June 2021 when the delta variant spread across the UK. Compared with the fixed-predictors model, the advantage of dynamic models is observed after several iterations of update.
Conclusions: With flexible data-driven predictors selection process, our dynamic modelling framework shows promises in predicting short-term changes in COVID-19 cases. The online application (COVIDPredLTLA) could assist decision-making for control measures and planning of healthcare capacity in future epidemic growths.
Competing Interests: Competing interestsThe authors declare the following competing interests: Y.L. reports grants from the Wellcome Trust, during the conduct of the study, and grants from WHO, outside the submitted work. H.N. reports grants from the Innovative Medicines Initiative, WHO, the National Institute for Health Research, Sanofi, and the Foundation for Influenza Epidemiology; and personal fees from the Bill & Melinda Gates Foundation, Janssen, ReViral, AbbVie, Sanofi, and the Foundation for Influenza Epidemiology, outside the submitted work. All other authors declare no competing interests.
(© The Author(s) 2022.)
Databáze: MEDLINE
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