Evidence map›Paper›PMID 39333386›Full record

ArticleScientific reports2024

Spatio-temporal distribution of rhinovirus types in Kenya: a retrospective analysis, 2014.

John Mwita Morobe, Everlyn Kamau, Martha M Luka, Nickson Murunga, Clement Lewa, Martin Mutunga, Godfrey Bigogo, Nancy Otieno, Bryan Nyawanda, Clayton Onyango and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

John Mwita MorobeEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya. jmwita@kemri-wellcome.org.
Everlyn KamauNuffield Department of Medicine, University of Oxford, Oxford, UK.
Martha M LukaEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.
Nickson MurungaEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.
Clement LewaEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.
Martin MutungaEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.
Godfrey BigogoKEMRI- Centre for Global Health Research, Kisumu, Kenya.
Nancy OtienoKEMRI- Centre for Global Health Research, Kisumu, Kenya.
Bryan NyawandaKEMRI- Centre for Global Health Research, Kisumu, Kenya.
Clayton OnyangoDivision of Global Health Protection, U.S Centers for Disease Control and Prevention (CDC), Centers for Global Health, Nairobi, Kenya.
D James NokesEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.
Charles N AgotiEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.
Patrick K MunywokiEpidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.

Funding

Wellcome TrustWellcome Trust 102975
6 · The paper itself

Abstract

The epidemiology and circulation patterns of various rhinovirus types within populations remains under-explored. We generated 803 VP4/VP2 gene sequences from rhinovirus-positive samples collected from acute respiratory illness (ARI) patients, including both in-patient and outpatient cases, between 1st January and 31st December 2014 from eleven surveillance sites across Kenya and used phylogenetics to characterise virus introductions and spread. RVs were detected throughout the year, with the highest detection rates observed from January to March and June to July. We detected a total of 114 of the 169 currently classified types. Our analysis revealed numerous virus introductions into Kenya characterized by local expansion and extinction, and extensive spatial mixing of types within the country due to the widespread transmission of the virus after an introduction. This work demonstrates that in a single year, the circulation of rhinovirus in Kenya was characterized by substantial genetic diversity, multiple introductions, and extensive geographical spread.

Indexed as

PhylogenyPicornaviridae InfectionsRhinovirusSpatio-Temporal AnalysisFemaleGenetic VariationHumansKenyaMaleRespiratory Tract InfectionsRetrospective StudiesKenyaPhylogeneticsRhinovirusSpatio-temporal distributionTransmission

Identifiers

PMID39333386
PMCPMC11436855

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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.