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Epidemiological Spatial Analysis of Animal Health Problems

Epidemiological Spatial Analysis of Animal Health Problems. Dirk Pfeiffer Professor of Veterinary Epidemiology Royal Veterinary College University of London. Objectives of Presentation. provide overview of spatial analysis in context of epidemiological investigations

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Epidemiological Spatial Analysis of Animal Health Problems

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  1. Epidemiological Spatial Analysis of Animal Health Problems Dirk PfeifferProfessor of Veterinary EpidemiologyRoyal Veterinary CollegeUniversity of London

  2. Objectives of Presentation • provide overview of spatial analysis in context of epidemiological investigations • from basics to advanced methods • describe structured approach towards spatial data analysis

  3. Epidemiology and Space • epidemiological investigation • person/animal • time and • space • spatial epidemiological analysis • visualisation -> no problem -> fun (?) • exploration, modelling -> more difficult, data dependence problems

  4. Attribute data Feature data GISDBMS Visualization Maps Describe patterns Exploration StatisticalSoftware Test hypotheses Modelling Framework for Spatial Data Analysis Databases

  5. GIS Data geographic layers disease outbreaks vector road network GeographicInformationSystem land parcels raster topography land use real world

  6. Attribute data Feature data Databases Visualization Describe patterns Exploration StatisticalSoftware Test hypotheses Modelling Framework for Spatial Data Analysis GISDBMS Maps

  7. Visualization • show actual values • 2D, 3D, more dimensional • points / areas • coloured points / areas (choropleth) • map series (adds time) -> animate (movie) • generate continuous representations of point data • interpolation • smoothing

  8. The Possum and TB

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

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

  11. Kernel Smoothing • generate continuous surface from point data showing density of cases • method • symmetric surface placed over each point • choice of kernel functions (normal, triangular, quartic) -> does not make much difference as long as symmetrical • sum 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 Herddensity

  15. Mapping Area Data- Counts and Proportions • crude risks / rates • standardised mortality ratio • empirical Bayes’ estimation

  16. Standardised Mortality Ratio • crude measure of relative risk • method • estimate expected counts for each polygon by multiplying population at risk with risk for whole region • divide observed count by expected for each polygon • generate map • disadvantage • small counts may result in extreme values for SMR • small counts -> large standard errors

  17. TB Prevalence TB SMR Example – TB Frequency Estimates

  18. Empirical Bayes’ Estimation • adjusted risks, rates or ratios • use knowledge about overall pattern of risk to smooth local risk assessment • incorporate confidence in estimate into calculation • prior derived from whole area or neighbourhood

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

  20. Attribute data Feature data Databases Visualization Test hypotheses Modelling Framework for Spatial Data Analysis GISDBMS Maps Describe patterns Exploration StatisticalSoftware

  21. Exploration • describe and quantify spatial structure • some hypothesis testing • cluster detection (cluster alarms) • spatial dependence • methods • point / aggregate data • global / local statistics

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