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

被引：16

作者：

Celani, Alessandro ^{[1
]}

Giudici, Paolo ^{[2
]}

机构：

[1] Polytech Univ Marche, Dipartimento Sci Econ & Sociali, Piazzale Raffaele Martelli 8, I-60121 Ancona, Italy

[2] Univ Pavia, Dipartimento Sci Econ & Aziendali, Via San Felice Al Monastero 5, I-27100 Pavia, Italy

来源：

SPATIAL STATISTICS | 2022年 / 49卷

关键词：

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

D O I：

10.1016/j.spasta.2021.100528

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：15

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