Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru

被引：2

作者：

Galarza, Cesar Raul Castro ^{[1
]}

Sanchez, Omar Nolberto Diaz ^{[1
]}

Pimentel, Jonatha Sousa ^{[2
]}

Bulhoes, Rodrigo ^{[3
]}

Lopez-Gonzales, Javier Linkolk ^{[1
]}

Rodrigues, Paulo Canas ^{[3
]}

机构：

[1] Univ Peruana Union, Escuela Posgrad, Lima 15468, Peru

[2] Univ Fed Pernambuco, Dept Stat, BR-50740540 Recife, PE, Brazil

[3] Univ Fed Bahia, Dept Stat, BR-40170110 Salvador, BA, Brazil

来源：

ENTROPY | 2024年 / 26卷 / 06期

关键词：

COVID-19; spatio-temporal modeling; areal unit data; spatio-temporal generalized linear model; Bayesian statistics;

D O I：

10.3390/e26060474

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.

引用

页数：13

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