Courses Details

EPID778 Spatial Statistics for Epidemiological Data

  • Graduate level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Berrocal, Veronica
  • Last offered Summer 2016
  • Advisory Prerequisites: Previous expericne with R is preferred, not required
  • Description: With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data, covering modeling approaches for the two different types of spatial data: point-referenced data, where the geographical coordinates of the observations have been recorded; and areal-averaged data, where summary statistics (e.g., number of disease cases by county, zip code, etc.) are reported for each areal unit. Topics covered include: exploratory analysis for spatial data, covariance functions, kriging, spatial regression; disease mapping, spatial smoothing; point processes, assessment of clustering, and cluster detection. Each lecture will feature a lab component, during which spatial analyses of datasets, made available to the participants, will be performed using the publically available R statistical software
  • Course Goals: With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data
  • Competencies: With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data