A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba

Dries De Witte; Ariel Alonso Abad; Geert Molenberghs; Geert Verbeke; Lizet Sanchez; Pedro Mas-Bermejo; Thomas Neyens

doi:10.1016/j.sste.2023.100588

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba

Spat Spatiotemporal Epidemiol. 2023 Jun:45:100588. doi: 10.1016/j.sste.2023.100588. Epub 2023 May 10.

Authors

Dries De Witte¹, Ariel Alonso Abad², Geert Molenberghs², Geert Verbeke², Lizet Sanchez³, Pedro Mas-Bermejo⁴, Thomas Neyens²

Affiliations

¹ L-BioStat, KU Leuven, Leuven, 3000, Belgium. Electronic address: dries.dewitte@kuleuven.be.
² L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium.
³ Cuban National Group of Epidemiology and Modeling of the COVID-19 Pandemic, Center of Molecular Immunology, Havana, 11 600, Cuba.
⁴ Cuban National Group of Epidemiology and Modeling of the COVID-19 Pandemic, Institute "Pedro Kouri", Havana, 11 600, Cuba.

Abstract

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Keywords: Bayesian inference; COVID-19; Joint models; Multivariate spatio-temporal modeling.

MeSH terms

Bayes Theorem
COVID-19*
Cuba / epidemiology
Humans
Incidence
Spatio-Temporal Analysis