Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms. Wang, X., Dong, Y., Thompson, W. D., Nair, H., & Li, Y. Communications medicine, 2:119, 2022. Place: England
doi  abstract   bibtex   
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.
@article{wang_short-term_2022,
	title = {Short-term local predictions of {COVID}-19 in the {United} {Kingdom} using dynamic supervised machine learning algorithms.},
	volume = {2},
	copyright = {© The Author(s) 2022.},
	issn = {2730-664X},
	doi = {10.1038/s43856-022-00184-7},
	abstract = {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.},
	language = {eng},
	journal = {Communications medicine},
	author = {Wang, Xin and Dong, Yijia and Thompson, William David and Nair, Harish and Li, You},
	year = {2022},
	pmid = {36168444},
	pmcid = {PMC9509378},
	note = {Place: England},
	keywords = {Disease prevention, Epidemiology},
	pages = {119},
}

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