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SpatialEpiApp: A Shiny web application for the analysis of spatial and spatio-temporal disease data
1Centre for Health Informatics, Computing and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, LA1 4YW, United Kingdom.
Spatial and Spatio-Temporal Epidemiology
|November 8, 2017
Summary
SpatialEpiApp is a user-friendly Shiny web application making spatial and spatio-temporal disease surveillance accessible. It integrates disease mapping and cluster detection without requiring programming skills.
Area of Science:
- Public Health
- Biostatistics
- Geographic Information Systems (GIS)
Background:
- Statistical methods for spatial and spatio-temporal disease data analysis have advanced public health surveillance.
- Accessibility remains a challenge for researchers lacking programming expertise in specialized software.
Purpose of the Study:
- To introduce SpatialEpiApp, a Shiny web application designed to simplify spatial and spatio-temporal disease surveillance.
- To integrate common health surveillance methods: disease mapping and cluster detection.
Main Methods:
- SpatialEpiApp utilizes R-INLA for fitting Bayesian models to estimate disease risk and uncertainty.
- SaTScan is employed for the detection of disease clusters.
- The application requires case, population, and optional covariate data for study areas and dates.
Main Results:
- SpatialEpiApp provides an accessible platform for disease mapping and cluster detection.
- It enables the fitting of Bayesian models and the identification of disease clusters without programming knowledge.
- Interactive visualizations and reports are generated for analyzed data.
Conclusions:
- SpatialEpiApp enhances public health surveillance by democratizing advanced statistical methods.
- The application empowers researchers without programming skills to perform complex spatial and spatio-temporal analyses.
- It facilitates a deeper understanding of disease patterns and risk through integrated tools.
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