Epidemiological Spatial Analysis of Animal Health Problems Dirk Pfeiffer Professor of Veterinary Epidemiology Royal Veterinary College University of London

2 Objectives of Presentation zprovide overview of spatial analysis in context of epidemiological investigations yfrom basics to advanced methods zdescribe structured approach towards spatial data analysis

3 Epidemiology and Space zepidemiological investigation yperson/animal ytime and yspace zspatial epidemiological analysis yvisualisation -> no problem -> fun (?) yexploration, modelling -> more difficult, data dependence problems

4 Framework for Spatial Data Analysis Visualization Exploration Modelling Attribute data Feature data Databases MapsDescribe patterns Test hypothese s GIS DBMS Statistical Software

5 GIS Data Geographic Information System land use real world topography land parcels road network disease outbreaks vector raster geographic layers

6 Framework for Spatial Data Analysis Attribute data Feature data Databases Visualization Maps GIS DBMS Exploration Describe patterns Statistical Software Modelling Test hypothese s

7 Visualization zshow actual values y2D, 3D, more dimensional xpoints / areas xcoloured points / areas (choropleth) xmap series (adds time) -> animate (movie) zgenerate continuous representations of point data yinterpolation ysmoothing

8 The Possum and TB

9 Spatio-temporal Distribution of REA Types in Possum TB Study REA Type 4 REA Type 4b REA Type 4a REA Type 10

10 Maps of Point Locations Locations of all cattle herds tested in 1999 Locations of test-positive cattle herds tested in 1999

11 Kernel Smoothing zgenerate continuous surface from point data showing density of cases zmethod ysymmetric surface placed over each point xchoice of kernel functions (normal, triangular, quartic) -> does not make much difference as long as symmetrical ysum distributions at any location -> density distribution

12 Kernel Density Maps (30km bandwidth, 10km grid)

13 Kernel Density Ratio Map (30 km bandwidth, 10 km grid)

14 Times Series of Maps - Herd Level TB Infection Risk in G. Britain Herd density

15 Mapping Area Data - Counts and Proportions zcrude risks / rates zstandardised mortality ratio zempirical Bayes’ estimation

16 Standardised Mortality Ratio zcrude measure of relative risk zmethod yestimate expected counts for each polygon by multiplying population at risk with risk for whole region ydivide observed count by expected for each polygon ygenerate map zdisadvantage ysmall counts may result in extreme values for SMR ysmall counts -> large standard errors

17 Example – TB Frequency Estimates TB Prevalence TB SMR

18 Empirical Bayes’ Estimation zadjusted risks, rates or ratios zuse knowledge about overall pattern of risk to smooth local risk assessment zincorporate confidence in estimate into calculation zprior derived from whole area or neighbourhood

19 Example – Bayes’ Estimates of TB Risk Crude TB Prevalence Empirical Bayes’ TB Prevalence

20 Framework for Spatial Data Analysis Exploration Modelling Describe patterns Test hypothese s Statistical Software Attribute data Feature data Databases Visualization Maps GIS DBMS

21 Exploration zdescribe and quantify spatial structure ysome hypothesis testing xcluster detection (cluster alarms) xspatial dependence zmethods ypoint / aggregate data yglobal / local statistics