TY - JOUR
T1 - SpatialEpiApp: A Shiny web application for the analysis of spatial and spatio-temporal disease data
AU - Moraga, Paula
N1 - Generated from Scopus record by KAUST IRTS on 2021-03-16
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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.
AB - 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.
UR - https://linkinghub.elsevier.com/retrieve/pii/S187758451730062X
UR - http://www.scopus.com/inward/record.url?scp=85028732132&partnerID=8YFLogxK
U2 - 10.1016/j.sste.2017.08.001
DO - 10.1016/j.sste.2017.08.001
M3 - Article
SN - 1877-5853
VL - 23
SP - 47
EP - 57
JO - Spatial and Spatio-temporal Epidemiology
JF - Spatial and Spatio-temporal Epidemiology
ER -