Evidence map›Paper›PMID 38422234›Full record

ArticleRevista brasileira de epidemiologia = Brazilian journal of epidemiology2024

Was the COVID-19 epidemic synchronous in space? An analysis in the health regions of the Rio de Janeiro state, 2020-2022.

Léa de Freitas Amaral, Raquel Martins Lana, Leonardo Soares Bastos

Abstract read
In one paragraph

Article in Revista brasileira de epidemiologia = Brazilian journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Léa de Freitas AmaralFundação Oswaldo Cruz, National School of Public Health - Rio de Janeiro (RJ), Brazil.ORCID http://orcid.org/0000-0002-0914-7637
Raquel Martins LanaBarcelona Supercomputing Center - Barcelona, Spain.ORCID http://orcid.org/0000-0002-7573-1364
Leonardo Soares BastosFundação Oswaldo Cruz, Scientific Computing Program - Rio de Janeiro (RJ), Brazil.ORCID http://orcid.org/0000-0002-1406-0122

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo analyze the spatio-temporal dynamics of COVID-19 in the Rio de Janeiro state within the nine health regions, between March 2020 and December 2022.

methodsThe Poisson model with random effects was used to smooth and estimate the incidence of COVID-19 hospitalizations reported in the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) to verify the synchronicity of the epidemic in the state.

resultsThe COVID-19 epidemic in the state is characterized by the presence of seven peaks during the analyzed period corresponding to seven found. An asynchrony in hospitalizations was identified, varying according to the different virus variants in the nine health regions of the state. The incidence peaks of hospitalizations ranged from 1 to 12 cases per 100,000 inhabitants during the pandemic.

conclusionThis spatio-temporal analysis is applicable to other scenarios, enabling monitoring and decision-making for the control of epidemic diseases in different areas.

Indexed as

COVID-19BrazilHumansIncidencePandemicsSpatio-Temporal Analysis

Identifiers

PMID38422234
PMCPMC10896236

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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.