Evidence map›Paper›PMID 31364556›Full record

ArticleEpidemiology and infection2019

Spatial diffusion of the 2015-2016 Zika, dengue and chikungunya epidemics in Rio de Janeiro Municipality, Brazil.

A P R Dalvi, J U Braga

Abstract read
In one paragraph

Article in Epidemiology and infection, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic.International journal of environmental research and public health · 2022
    Article
  5. Article
  6. 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

2 authors.

A P R DalviEscola Nacional de Saude Publica Sergio Arouca,Fundação Oswaldo Cruz,Rio de Janeiro,Brazil.
J U BragaEscola Nacional de Saude Publica Sergio Arouca,Fundação Oswaldo Cruz,Rio de Janeiro,Brazil.ORCID 0000-0001-5247-007X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Different countries, especially Brazil, that have faced recurrent dengue epidemics for decades and chikungunya epidemics since 2014, have had to restructure their health services to combat a triple epidemic of arboviruses - Zika, dengue and Chikungunya - transmitted by the same vector, mainly Aedes aegypti, in 2015-2016. Several efforts have been made to better understand these three arboviruses. Spatial analysis plays an important role in the knowledge of disease dynamics. The knowledge of the patterns of spatial diffusion of these three arboviruses during an epidemic can contribute to the planning of surveillance actions and control of these diseases. This study aimed to identify the spatial diffusion processes of these viruses in the context of the triple epidemic in 2015-2016 in Rio de Janeiro, Brazil. Two study designs were used: cross-sectional and ecological. Sequential Kernel maps, nearest-neighbour ratios calculated cumulatively over time, Moran global autocorrelation correlograms, and local autocorrelation changes over time were used to identify spatial diffusion patterns. The results suggested an expansion diffusion pattern for the three arboviruses during 2015-2016 in Rio de Janeiro. These findings can be considered for more effective control measures and for new studies on the dynamics of these three arboviruses.

Indexed as

AnimalsBrazilChikungunya FeverCross-Sectional StudiesDengueDisease Transmission, InfectiousFemaleHumansMaleMosquito VectorsRetrospective StudiesUrban PopulationZika Virus InfectionChikungunya virusdengue virusspatio temporal analysisZika virus

Identifiers

PMID31364556
PMCPMC6625212

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.