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

Xin Wang; Yijia Dong; William David Thompson; Harish Nair; You Li

doi:10.1038/s43856-022-00184-7

Communications Medicine (Sep 2022)

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

Xin Wang,
Yijia Dong,
William David Thompson,
Harish Nair,
You Li

Affiliations

Xin Wang: School of Public Health, Nanjing Medical University
Yijia Dong: Edinburgh Medical School, College of Medicine and Veterinary Medicine, The University of Edinburgh
William David Thompson: Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham
Harish Nair: Centre for Global Health, The University of Edinburgh
You Li: School of Public Health, Nanjing Medical University

DOI: https://doi.org/10.1038/s43856-022-00184-7
Journal volume & issue: Vol. 2, no. 1
pp. 1 – 8

Abstract

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Wang et al. predict local COVID-19 incidence one to three weeks ahead based on symptom search trends, population mobility, and vaccination coverage. Their dynamic model is more accurate than naïve and fixed-predictor models.

Published in Communications Medicine

ISSN: 2730-664X (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Medicine
Website: https://www.nature.com/commsmed/

About the journal