Data · dataset · 2019
Demonstration data for: "An interactive data visualisation application to investigate nosocomial transmission of infections"
Listed in LSHTM Data Compass
Description
Background
Healthcare-associated infections represent a major threat to patient, staff and visitor safety. Identification of episodes that are likely to have resulted from nosocomial transmission has important implications for infection control. Routinely collected data on ward admissions and sample dates, combined with pathogen genomic information could provide useful insights.
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We describe a novel, open-source, application for visualising these data, and demonstrate its utility for investigating nosocomial transmission using a case study of a large outbreak of norovirus infection.
Methods
We developed the application using Shiny, a web application framework for R. For the norovirus case study, cases were defined as patients who had a faecal sample collected at the hospital in a winter season that tested positive for norovirus. Patient demographics and ward admission dates were extracted from hospital systems. Detected norovirus strains were genotyped and further characterised through sequencing of the hypervariable P2 domain.
The most commonly detected sub-strain was visualised using the interactive application. Results: There were 156 norovirus-positive specimens collected from 107 patients. The most commonly detected sub-strain affected 30 patients in five wards.
We used the interactive application to produce three visualisations: a bar chart, a timeline, and a schematic ward plan highlighting plausible transmission links. Visualisations showed credible links between cases on the elderly care ward. Conclusions: Use of the interactive application provided insights into transmission in this large nosocomial outbreak of norovirus, highlighting where infection control practices worked well or could be improved.
This is a flexible tool that could be used for investigation of any infection in any hospital by interactively changing parameters. Challenges include integration with hospital systems for extracting data. Prospective use of this application could inform better infection control in real time.
Links
Where it is published
- DOI doi.org/10.6084/m9.figshare.8241530.v2 ↗
DOI / persistent id · from lshtm ac uk
Catalogue records · 1
- OAI-PMH record datacompass.lshtm.ac.uk/cgi/oai2?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3… ↗
metadata API · from lshtm ac uk
Topics
- From keywords
- Earth & Environmental Science · Humanities · Life Sciences · Medicine & Health · Social Science
- Inferred from text
- Epidemiology 72% · Sequencing 75%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| LSHTM Data Compass | oai:datacompass.lshtm.ac.uk:1309 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4202 | enrichment · lshtm ac uk | taxonomy-embedding@1.0.0 | title+keywords+description (72%) |
| concepts[field].local:field:earth-environmental | mapping · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | |
| concepts[modality].local:modality:sequencing | enrichment · lshtm ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | /metadata/dc/description |
| publication_date | source · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | |
| title | source · lshtm ac uk | connector:lshtm_ac_uk@1.0.0 | /metadata/dc/title |