Evidence map›Paper›PMID 38920483›Full record

ArticleEntropy (Basel, Switzerland)2024

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

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

César Raúl Castro GalarzaEscuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru.ORCID 0000-0003-1697-0514
Omar Nolberto Díaz SánchezEscuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru.ORCID 0000-0001-6276-1145
Jonatha Sousa PimentelDepartment of Statistics, Federal University of Pernambuco, Recife 50740-540, PE, Brazil.ORCID 0000-0003-2982-5383
Rodrigo BulhõesDepartment of Statistics, Federal University of Bahia, Salvador 40170-110, BA, Brazil.ORCID 0000-0003-3668-6860
Javier Linkolk López-GonzalesEscuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru.ORCID 0000-0003-0847-0552
Paulo Canas RodriguesDepartment of Statistics, Federal University of Bahia, Salvador 40170-110, BA, Brazil.ORCID 0000-0002-1248-9910

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

areal unit dataBayesian statisticsCOVID-19spatio-temporal generalized linear modelspatio-temporal modeling

Identifiers

PMID38920483
PMCPMC11202420

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.