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SpatialEpiApp:A Shiny Web Application for the Analysis of Spatial and Spatio-Temporal Disease Data

Moraga-Serrano, Paula (2017) SpatialEpiApp:A Shiny Web Application for the Analysis of Spatial and Spatio-Temporal Disease Data. Spatial and Spatio-temporal Epidemiology, 23. pp. 47-57. ISSN 1877-5845

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    Abstract

    During last years, public health surveillance has been facilitated by the existence of several packages implementing statistical methods for the analysis of spatial and spatio-temporal disease data. However, these methods are still inaccesible for many researchers lacking the adequate programming skills to effectively use the required software. In this paper we present SpatialEpiApp, a Shiny web application that integrate two of the most common approaches in health surveillance: disease mapping and detection of clusters. SpatialEpiApp is easy to use and does not require any programming knowledge. Given information about the cases, population and optionally covariates for each of the areas and dates of study, the application allows to fit Bayesian models to obtain disease risk estimates and their uncertainty by using R-INLA, and to detect disease clusters by using SaTScan. The application allows user interaction and the creation of interactive data visualizations and reports showing the analyses performed.

    Item Type: Journal Article
    Journal or Publication Title: Spatial and Spatio-temporal Epidemiology
    Additional Information: This is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, 23, 2017 DOI: 10.1016/j.sste.2017.08.001
    Subjects: ?? DISEASE MAPPINGCLUSTERSSHINYINLASATSCAN ??
    Departments: Faculty of Health and Medicine > Medicine
    ID Code: 87484
    Deposited By: ep_importer_pure
    Deposited On: 30 Aug 2017 11:00
    Refereed?: Yes
    Published?: Published
    Last Modified: 30 Apr 2019 02:37
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/87484

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