LAUSR

Abstract Background Whole-genome sequencing, and other molecular methods, can be used to identify potential chains of transmission of infections in hospitals. However, integrating this information with epidemiological data to guide control measures is challenging. We aimed to produce an interactive application for visualisation of hospital infections for use by infection control teams and researchers. Methods We developed this application using Shiny, the web application framework for R. The minimum data set required to run the application comprises admission and sample dates and the ward on which the sample was taken. Optional additional data are dates of patient ward transfers, descriptive patient characteristics including genetically defined clusters, and genetic distances between infections. We demonstrate this application with a case-study of 242 influenza samples from a UK hospital (September, 2012, to March, 2014), which were sequenced as part of the ICONIC project. Findings The application presents three data visualisations: one displays epidemic curves of the numbers of cases of the infection over time, both overall and separately for each ward. Interactive options allow the user to change graph scales and display subsets of the data. The second visualisation is a schematic representation of hospital wards that shows patient locations on a given date. It then highlights epidemiological and genetic links between patients. The third visualisation is an interactive timeline displaying the wards that patients were staying on and on which dates they were likely to be exposed to, or transmitting, an infection. Interpretation This novel application produces visual displays to aid interpretation of complex epidemiological and genomic data in hospital settings. It can be used to highlight areas in a hospital in which infections may have been transmitted, and to trace possible chains of transmission by highlighting patients who share epidemiological or genetic links. Advantages include its user-friendly interface and flexibility for use in any setting with similar data on any pathogen. A challenge is integration with existing hospital systems to facilitate data import, since some expertise is needed to extract appropriately formatted data from hospital systems. Funding Supported by the Health Innovation Challenge Fund (T5-355) (ICONIC), a parallel funding partnership between the Department of Health and the Wellcome Trust.