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

César Raúl Castro Galarza; Omar Nolberto Díaz Sánchez; Jonatha Sousa Pimentel; Rodrigo Bulhões; Javier Linkolk López-Gonzales; Paulo Canas Rodrigues

doi:10.3390/e26060474

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

César Raúl Castro Galarza, Omar Nolberto Díaz Sánchez, Jonatha Sousa Pimentel, Rodrigo Bulhões, Javier Linkolk López-Gonzales, Paulo Canas Rodrigues

Research Group Mathematical models in science and technology

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Article number	474
Journal	Entropy
Volume	26
Issue number	6
DOIs	https://doi.org/10.3390/e26060474
State	Published - Jun 2024

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title = "Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru",

abstract = "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{\textquoteright}s impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.",

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AU - Galarza, César Raúl Castro

AU - Sánchez, Omar Nolberto Díaz

AU - Pimentel, Jonatha Sousa

AU - Bulhões, Rodrigo

AU - López-Gonzales, Javier Linkolk

AU - Rodrigues, Paulo Canas

PY - 2024/6

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N2 - 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.

AB - 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.

KW - Bayesian statistics

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

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