Faculty: Veronica Berrocal1 , Annibale Biggeri2 , Dolores Catelan2 , Corrado Lagazio3
Affiliations: 1Department of Biostatistics, University of Michigan, Ann Arbor, USA 2Department of Statistics, Computer Science, Application “G.Parenti”, University of Florence, Florence, Italy. 3Department of Economics, University of Genoa, Genoa, Italy
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 proposed in the literature for the different types of spatial support, e.g. point-referenced data, where the geographical coordinates of the observations have been recorded; and areal-averaged data, where summary statistics are reported for each areal unit. Topics covered include: exploratory analysis for spatial data, covariance functions, kriging, spatial regression, spatially-varying coefficients; spatial analysis for areal data, spatial smoothing and disease mapping; 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 different statistical software, including STATA, R (downloadable at www.r-project.org) and WinBugs. Participants will be able to read and understand written English scientific material.
Location: Department of Statistics, Computer Science, Application “G.Parenti”, University of Florence, Florence, Italy. Viale Morgagni 59, 50134 Florence