ArticleThe Annals of regional science2022
A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden.
Article in The Annals of regional science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Time series anomaly detection in helpline call trends for early detection of COVID-19 spread across Sweden, 2020.Scientific reports · 2025Article
- JSTMapp: A web-based joint spatiotemporal modelling and mapping application for epidemiologists.PloS one · 2025Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
The three closely related COVID-19 outcomes of incidence, intensive care (IC) admission and death, are commonly modelled separately leading to biased estimation of the parameters and relatively poor forecasts. This paper presents a joint spatiotemporal model of the three outcomes based on weekly data that is used for risk prediction and identification of hotspots. The paper applies a pure spatiotemporal model consisting of structured and unstructured spatial and temporal effects and their interaction capturing the effects of the unobserved covariates. The pure spatiotemporal model limits the data requirements to the three outcomes and the population at risk per spatiotemporal unit. The empirical study for the 21 Swedish regions for the period 1 January 2020-4 May 2021 confirms that the joint model predictions outperform the separate model predictions. The fifteen-week-ahead spatiotemporal forecasts (5 May-11 August 2021) show a significant decline in the relative risk of COVID-19 incidence, IC admission, death and number of hotspots. Supplementary Information: The online version contains supplementary material available at 10.1007/s00168-022-01191-1.
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Registered trials
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