A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden

I Gede Nyoman Mindra Jaya; Henk Folmer; Johan Lundberg

doi:10.1007/s00168-022-01191-1

A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden

Ann Reg Sci. 2022 Nov 28:1-34. doi: 10.1007/s00168-022-01191-1. Online ahead of print.

Authors

I Gede Nyoman Mindra Jaya^{1

2}, Henk Folmer^{1

2}, Johan Lundberg³

Affiliations

¹ Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands.
² Statistics Department, Padjadjaran University, Bandung, Indonesia.
³ Department of Economics and Centre for Regional Science (CERUM), Umeå University, 901 87 Umeå, Sweden.

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.