Bayesian spatial modelling of disease vector data on danish farmland
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Bayesian spatial modelling of disease vector data on Danish farmland. Carsten Kirkeby Gerard Heuvelink Anders Stockmarr René Bødker. Biting midges. Culicoides obsoletus group Bloodsucking females 1400 species ~ 40 in Denmark 1-2mm Parasites: protozoans, nematodes

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Bayesian spatial modelling of disease vector data on Danish farmland

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Bayesian spatial modelling of disease vector data on danish farmland

Bayesian spatial modelling of disease vector data on Danish farmland

Carsten Kirkeby

Gerard Heuvelink

Anders Stockmarr

René Bødker


Biting midges

Biting midges

  • Culicoides obsoletus group

  • Bloodsucking females

  • 1400 species ~ 40 in Denmark

  • 1-2mm

  • Parasites: protozoans, nematodes

  • Virus: African Horse Sickness,

  • Akabane Virus etc.

Institute of Animal Health UK


Bluetongue virus

Bluetongue virus

  • Midge-borne

  • Infects ruminants

  • Northern Europe: 2006-2010

  • Symptoms: Fever, diarrhoea, reduced milk production

Institute of Animal Health UK


Schmallenberg virus

Schmallenberg virus

  • Midge-borne

  • Infects ruminants

  • Northern Europe: 2011 - ?

  • Symptoms: Fever, stillbirths, malformations, reduced milk production

Institute of Animal Health UK


Bayesian spatial modelling of disease vector data on danish farmland

Aim

  • How are vectors distributed in farmland?

  • Host animals

  • Tree cover

  • Temporal covariates

  • High/low risk areas

  • Optimization of vector surveillance

  • Input for simulation models


Field study

Field study

x


Field study1

Field study


Field study2

Field study


Bayesian spatial modelling of disease vector data on danish farmland

Data


Analysis

Analysis

Count data


Analysis1

Analysis

Spatial component

“Your neighbours influence you, but you also influence your neighbours.”

Charles Manski


Analysis2

Analysis

Temporal component

t

t-1


Analysis3

Analysis

R: geoRglm package – GLGM kriging

pois.krige.bayes()

Bayesian kriging for the poisson spatial model

Y ~ β + S(ρ) + ε

β = + + + + dayeffect + lag1


Analysis4

Analysis

Spatial correlation: Matérn covariance function

Φ


Analysis separate

Analysis - separate


Analysis simultaneous

Analysis - simultaneous


Analysis simultaneous1

Analysis - simultaneous


Analysis comparison

Analysis - comparison

-0.12

-0.33

Non-spatial

Poisson

regression

0.07

0.008


Analysis prediction

Analysis - prediction

1 km


Analysis temporal covariates

Analysis – temporal covariates


Findings

Findings

  • Quantify effects of cattle and pigs

  • No effect of forests

  • Quantify temporal covariates

  • Weak positive correlation with previous catch

  • More vectors at the pig farm than the cattle farm


Future

Future

  • Jackknife

  • Validation on other dataset


Acknowledgements

Acknowledgements

  • Thanks:

  • Ole Fredslund Christensen

  • Astrid Blok van Witteloostuijn


Bayesian spatial modelling of disease vector data on danish farmland

Thank you for your attention

Carsten Kirkeby

[email protected]


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