Courses Taught by Veronica Berrocal

BIOSTAT896: Spatial Statistics

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Berrocal, Veronica (Residential);
  • Not offered 2021-2022
  • Prerequisites: BIOSTAT 601, BIOSTAT 602, BIOSTAT 650, BIOSTAT 653
  • Description: This course will introduce the theory and methods of spatial and spatio-temporal statistics. It will present spatial and spatio-temporal statistical models and will discuss methods for inference on spatial processes within a geostatistical and a hierarchical Bayesian framework.
  • Syllabus for BIOSTAT896

EPID595: Applied Spatial Modeling

  • Graduate level
  • Both Online MPH and Online MS
  • This is a second year course for Online students
  • Winter term(s) for online MPH students; Winter term(s) for online MS students.
  • 3 credit hour(s) for online MPH students; 3 credit hour(s) for online MS students;
  • Instructor(s): Berrocal, Veronica (Online MPH); Berrocal, Veronica (Online MS);
  • Prerequisites: EPID 592 and EPID 594
  • Description: The large availability of geographically indexed health data, along with advances in computing, have enabled the development of statistical methods for the analysis of spatial epidemiological data. This course will introduce students to the most commonly used statistical models used to understand spatial variation in disease risk.
  • Learning Objectives: By the end of the course students will be able to: (i) Recognize different types of spatial data. (ii) Formulate research questions and determine the appropriate spatial statistical model to analyze the data. (iii) Understand the concept of spatial correlation and how to estimate it in point-level spatial data. (iv) Include spatial random effect in generalized linear models for the analysis of spatial data. (v) Interpret the results of a spatial generalized linear model. (vi) Perform spatial interpolation of point-referenced data over space to predict missing data at unsampled locations. (vii) Smooth disease rates and disease counts over space using multilevel hierarchical models. (viii) Understand the definition of a (disease) cluster. (ix) Obtain a kernel density estimate of the intensity function representing the likelihood of observing a disease case at a given location. (x) Identify clusters of disease cases via appropriate statistical methods. (xi) Formulate statistical models to characterize spatial variation in the distribution of disease cases.

EPID778: Spatial Statistics for Epidemiological Data

  • Graduate level
  • Residential
  • Summer term(s) for residential students;
  • 1 credit hour(s) for residential students;
  • Instructor(s): Berrocal, Veronica (Residential);
  • Last offered Summer 2016
  • Prerequisites: None
  • 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