A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany

TL;DR

This paper employs a hierarchical spatio-temporal Bayesian model to analyze COVID-19 spread dynamics in Spain, Italy, and Germany, revealing distinct temporal peaks and spatial risk heterogeneity across these countries.

Contribution

It introduces a hierarchical Bayesian spatio-temporal model specifically designed to analyze COVID-19 risk variations across multiple countries.

Findings

Temporal peaks occurred in April and August in all three countries.

Spain exhibited sharper declines and increases in COVID-19 trends.

Spatial heterogeneity of risk was most pronounced in Spain.

Abstract

The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February to mid September 2020. Using a hierarchical Bayesian framework, we found that the temporal trend of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in August. However decline and increase of the temporal trend seems to be sharper in Spain and smoother in Germany. The spatial heterogeneity of the relative risk of COVID-19 in Spain is also more pronounced than Italy and Germany.