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

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

SUBMITTER: Wang X 

PROVIDER: S-EPMC9509378 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature