Existing R packages for spatial analysis for spatial epidemiology

[sigmafelix]

1. SpatialEpi (CRAN Link)

   • Statistical tests (i.e., spatial clusters by Besag-Newell's and Kulldorff's methods)
   • Taking datasets that are readily analyzed are assumed
   • Inputs are points: polygons should be converted to centroids for analysis

2. SpatialEpiApp (Moraga 2017)

   • A R-Shiny app leveraging multiple external packages including INLA and SaTScan
   • Functions are mostly for statistical analysis, not for geospatial data handling to obtain variables from geospatial datasets
   • Seemingly not maintained by the author

3. aegis (Application for Epidemiological Geographic Information System) (Cho et al. 2020)

   • A R-Shiny app supporting cohort definition, temporal exploration, disease mapping, clustering, and interactive visualization of health outcomes/modeling results
   • Modeling module is based on R-INLA
   • Data standardization functions for Korean National Health Insurance System cohort data to be compliant of Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM)
   • Uses Database of Global Administrative Areas (GADM) for potential users outside Korea

• More to come

[sigmafelix]

4. exposome: not an explicitly spatial, but a good overview to manage exposome data

   • Developed by a team at the Barcelona Institute of Global Health (ISGlobal)
   • "Three table" approach: description, exposure, and phenotype in separate text files
   • Features missing data treatment, neat visualization functions, "Exposure/Enviroment/Exposome Wide Association Studies (ExWAS)" analysis
   • Vignette

5. cf. Scalable spatial join in R and Spark