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Jun, 2022;49 100528.

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

Alessandro Celani, Paolo Giudici
Author Information
  1. Alessandro Celani: Dipartimento di Scienze Economiche e Sociali, Polytechnic University of Marche, Piazzale Raffaele Martelli 8, 60121 Ancona, Italy.
  2. Paolo Giudici: Dipartimento di Scienze Economiche e Aziendali, University of Pavia, Via San Felice al Monastero 5, 27100 Pavia, Italy.
PMID: 34307007 DOI: 10.1016/j.spasta.2021.100528

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

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