Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity

Lee, D. , Robertson, C. and Marques, D. (2022) Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity. Spatial Statistics, 49, 100508. (doi: 10.1016/j.spasta.2021.100508) (PMID:33868908) (PMCID:PMC8035810)

Abstract

Modelling the small-area spatio-temporal dynamics of the Covid-19 pandemic is of major public health importance, because it allows health agencies to better understand how and why the virus spreads. However, in Scotland during the first wave of the pandemic testing capacity was severely limited, meaning that large numbers of infected people were not formally diagnosed as having the virus. As a result, data on confirmed cases are unlikely to represent the true infection rates, and due to the small numbers of positive tests these data are not available at the small-area level for confidentiality reasons. Therefore to estimate the small-area dynamics in Covid-19 incidence this paper analyses the spatio-temporal trends in telehealth data relating to Covid-19, because during the first wave of the pandemic the public were advised to call the national telehealth provider NHS 24 if they experienced symptoms of the virus. Specifically, we propose a multivariate spatio-temporal correlation model for modelling the proportions of calls classified as either relating to Covid-19 directly or having related symptoms, and provide software for fitting the model in a Bayesian setting using Markov chain Monte Carlo simulation. The model was developed in partnership with the national health agency Public Health Scotland, and here we use it to analyse the spatio-temporal dynamics of the first wave of the Covid-19 pandemic in Scotland between March and July 2020, specifically focusing on the spatial variation in the peak and the end of the first wave.

• Item Type: Articles
• Keywords: COVID-19 pandemic, Gaussian Markov random field models, Scotland, telehealth data.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lee, Professor Duncan
• Authors: Lee, D., Robertson, C., and Marques, D.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Spatial Statistics
• Publisher: Elsevier
• ISSN: 2211-6753
• ISSN (Online): 2211-6753
• Published Online: 10 April 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Spatial Statistics 49: 100508
• Publisher Policy: Reproduced under a Creative Commons License