Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru
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 i...
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| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | Ver en el sistema de origen Ver en el sistema de origen Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
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| Sumario: | 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. |
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