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

被引：14
|
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.
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页数：15
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