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Data for Human rhinovirus spatial-temporal epidemiology in rural coastal Kenya, 2015-2016, observed through outpatient surveillance

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Morobe, John Mwita, Nyiro, Joyce U., Brand, Samuel, Kamau, Everlyn, Munywoki, Patrick K., Agoti, Charles N. and Nokes, D. James (2018) Data for Human rhinovirus spatial-temporal epidemiology in rural coastal Kenya, 2015-2016, observed through outpatient surveillance. [Dataset]

Research output not available from this repository, contact author.
Official URL: https://doi.org/10.7910/DVN/DUQNDX

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Abstract

Background
Human rhinovirus (HRV) is the predominant cause of upper respiratory tract infections, resulting in a significant public health burden. The virus circulates as many different types (~160), each generating strong homologous, but weak heterotypic, immunity. The influence of these features on transmission patterns of HRV in the community is understudied.

Methods
Nasopharyngeal swabs were collected from patients with symptoms of acute respiratory infection (ARI) at nine out-patient facilities across a Health and Demographic Surveillance System between December 2015 and November 2016. HRV was diagnosed by real-time RT-PCR, and the VP4/VP2 genomic region of the positive samples sequenced. Phylogenetic analysis was used to determine the HRV types. Classification models and G-test statistic were used to investigate HRV type spatial distribution. Demographic characteristics and clinical features of ARI were also compared.

Results
Of 5,744 NPS samples collected, HRV was detected in 1057 (18.4%), of which 817 (77.3%) were successfully sequenced. HRV species A, B and C were identified in 360 (44.1%), 67 (8.2%) and 390 (47.7%) samples, respectively. In total, 87 types were determined: 39, 10 and 38 occurred within species A, B and C, respectively. HRV types presented heterogeneous temporal patterns of persistence. Spatially, identical types occurred over a wide distance at similar times, but there was statistically significant evidence for clustering of types between health facilities in close proximity or linked by major road networks.

Conclusion
This study records a high prevalence of HRV in out-patient presentations exhibiting high type diversity. Patterns of occurrence suggest frequent and independent community invasion of different types. Temporal differences of persistence between types may reflect variation in type-specific population immunity. Spatial patterns suggest either rapid spread or multiple invasions of the same type, but evidence of similar types amongst close health facilities, or along road systems, indicate type partitioning structured by local spread.

Item Type: Dataset
Subjects: Q Science > QR Microbiology > QR355 Virology
Divisions: Faculty of Science > Life Sciences (2010- )
Type of Data: Observational data
Library of Congress Subject Headings (LCSH): Rhinoviruses -- Epidemiology -- Kenya, Respiratory infections -- Kenya, Rural health -- Kenya
Publisher: Harvard Dataverse
Official Date: 21 September 2018
Dates:
DateEvent
21 September 2018Published
Collection date:
Date fromDate to
December 2015November 2016
Status: Not Peer Reviewed
Publication Status: Published
Media of Output: .do .tab .fas .m .ows .xls
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: University of Warwick
Description:

Data record consists of 8 data files, in .do, .tab .fas, .m, .ows and .xls format, as well as a readme file, separate data codebook, and .tiff file showing workflow.
Access to certain data is restricted as they contain personally identifiable information. Accompanying documentation is available under open access.
Phylogenetic analysis was conducted in MEGA v.6.0 with maximum likelihood methods under the GTR model and branch support was assessed using 1000 bootstrap iterations. Types were assigned based on >90% nucleotide similarity to rhinovirus prototype sequences (also referred to as reference sequences) available in GenBank or phylogenetic clustering with reference sequences (with a bootstrap support value above 70%). Statistical analysis was conducted using STATA version 13.1 to compare the demographic and clinical features in the HRV species. Classification models and G-test statistics were used to investigate the spatial distribution of HRV types in the KHDSS. All scripts used during the spatial analysis are described in the readme file.

RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
102975Wellcome Trusthttp://dx.doi.org/10.13039/100010269
107769Wellcome Trusthttp://dx.doi.org/10.13039/100010269
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Contributors:
ContributionNameContributor ID
Contact PersonMorobe, John MwitaUNSPECIFIED
Data managerOtieno, Grieven P.UNSPECIFIED

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