Epidemiological Spatial Analysis of Animal Health

Problems

Dirk PfeifferProfessor of Veterinary Epidemiology

Royal Veterinary CollegeUniversity of London

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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

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Epidemiology and Space

epidemiological investigation person/animal time and space

spatial epidemiological analysis visualisation -> no problem -> fun (?) exploration, modelling -> more difficult,

data dependence problems

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Framework for Spatial Data Analysis

Visualization

Exploration

Modelling

Attribute data

Feature data

Databases

Maps

Describe patterns

Test hypothese

s

GISDBMS

StatisticalSoftware

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GIS Data

GeographicInformation

System

land use

real world

topography

land parcels

road network

disease outbreaks

vect

orra

ster

geographic layers

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Framework for Spatial Data Analysis

Attribute data

Feature data

Databases

VisualizationMapsGIS

DBMS

ExplorationDescribe patterns

StatisticalSoftware

ModellingTest

hypotheses

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Visualization

show actual values 2D, 3D, more dimensional

points / areascoloured points / areas (choropleth)map series (adds time) -> animate (movie)

generate continuous representations of point data interpolation smoothing

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The Possum and TB

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Spatio-temporal Distribution of REA Types in Possum TB Study

REAType 4

REAType 4b

REAType 4a

REAType 10

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Maps of Point Locations

Locations of all cattle herds tested in 1999

Locations of test-positive cattle

herds tested in 1999

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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

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

Herd density0 - 0.0970.097 - 0.1940.194 - 0.2920.292 - 0.3890.389 - 0.4860.486 - 0.5830.583 - 0.6810.681 - 0.7780.778 - 0.875No Data

TB herd density0 - 0.0110.011 - 0.0210.021 - 0.0320.032 - 0.0430.043 - 0.0530.053 - 0.0640.064 - 0.0750.075 - 0.0850.085 - 0.096No Data

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

TB risk00 - 0.050.05 - 0.080.08 - 0.110.11 - 0.130.13 - 0.160.16 - 0.190.19 - 0.210.21 - 0.24No Data

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

Herddensity

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Mapping Area Data- Counts and Proportions

crude risks / ratesstandardised mortality ratioempirical Bayes’ estimation

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Standardised Mortality Ratio

crude measure of relative riskmethod

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 mapdisadvantage

small counts may result in extreme values for SMR small counts -> large standard errors

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Example – TB Frequency Estimates

Legend00 - 0.040.04 - 0.080.08 - 0.10.1 - 0.15

TB Prevalence

Legend00 - 0.040.04 - 11 - 22 - 4

TB SMR

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Empirical Bayes’ Estimation

adjusted risks, rates or ratiosuse knowledge about overall pattern

of risk to smooth local risk assessment

incorporate confidence in estimate into calculation

prior derived from whole area or neighbourhood

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Example – Bayes’ Estimates of TB Risk

Legend00 - 0.040.04 - 0.080.08 - 0.10.1 - 0.15

Crude TB Prevalence Empirical Bayes’ TB Prevalence

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Framework for Spatial Data Analysis

Exploration

Modelling

Describe patterns

Test hypothese

s

StatisticalSoftware

Attribute data

Feature data

Databases

VisualizationMapsGIS

DBMS

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Exploration

describe and quantify spatial structure some hypothesis testing

cluster detection (cluster alarms)spatial dependence

methods point / aggregate data global / local statistics