ArticleSpatial statistics2022
Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity.
Article in Spatial statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 19 citations in OpenAlex.
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
- Are at-risk sociodemographic attributes stable across COVID-19 transmission waves?Spatial and spatio-temporal epidemiology · 2023Article
- A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.Spatial and spatio-temporal epidemiology · 2023Article
- Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting.Spatial statistics · 2023Article
- Bayesian disease mapping: Past, present, and future.Spatial statistics · 2022Article
- Quantifying the impact of air pollution on Covid-19 hospitalisation and death rates in Scotland.Spatial and spatio-temporal epidemiology · 2022Article
- Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe.Spatial statistics · 2022Article
- Spatial clustering behaviour of Covid-19 conditioned by the development level: Case study for the administrative units in Romania.Spatial statistics · 2022Article
- Do spatiotemporal units matter for exploring the microgeographies of epidemics?Applied geography (Sevenoaks, England) · 2022Article
- Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million.Chaos, solitons, and fractals · 2022Article
- A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy.Spatial statistics · 2022Article
- Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories.AIMS public health · 2022Article
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Authors and funding
3 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
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.
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