Endemic-epidemic models to understand COVID-19 spatio-temporal evolution

Alessandro Celani; Paolo Giudici

doi:10.1016/j.spasta.2021.100528

Endemic-epidemic models to understand COVID-19 spatio-temporal evolution

Spat Stat. 2022 Jun:49:100528. doi: 10.1016/j.spasta.2021.100528. Epub 2021 Jul 12.

Authors

Alessandro Celani¹, Paolo Giudici²

Affiliations

¹ Dipartimento di Scienze Economiche e Sociali, Polytechnic University of Marche, Piazzale Raffaele Martelli 8, 60121 Ancona, Italy.
² Dipartimento di Scienze Economiche e Aziendali, University of Pavia, Via San Felice al Monastero 5, 27100 Pavia, Italy.

Abstract

We propose an endemic-epidemic model: a negative binomial space-time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterized by similar non-pharmaceutical policy interventions.

Keywords: COVID-19; Contagion models; Multivariate statistics; Poisson processes; Spatio-temporal models.