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

Bayesian spatial modelling of disease vector data on Danish farmland

Carsten Kirkeby

Gerard Heuvelink

Anders Stockmarr

René Bødker


Biting midges
Biting midges farmland

  • 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 farmland

  • Midge-borne

  • Infects ruminants

  • Northern Europe: 2006-2010

  • Symptoms: Fever, diarrhoea, reduced milk production

Institute of Animal Health UK


Schmallenberg virus
Schmallenberg virus farmland

  • Midge-borne

  • Infects ruminants

  • Northern Europe: 2011 - ?

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

Institute of Animal Health UK


Aim farmland

  • 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 farmland

x


Field study1
Field study farmland


Field study2
Field study farmland


Data farmland


Analysis
Analysis farmland

Count data


Analysis1
Analysis farmland

Spatial component

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

Charles Manski


Analysis2
Analysis farmland

Temporal component

t

t-1


Analysis3
Analysis farmland

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 farmland

Spatial correlation: Matérn covariance function

Φ





Analysis comparison
Analysis - comparison farmland

-0.12

-0.33

Non-spatial

Poisson

regression

0.07

0.008


Analysis prediction
Analysis - prediction farmland

1 km



Findings
Findings farmland

  • 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 farmland

  • Jackknife

  • Validation on other dataset


Acknowledgements
Acknowledgements farmland

  • Thanks:

  • Ole Fredslund Christensen

  • Astrid Blok van Witteloostuijn


Thank you farmland for your attention

Carsten Kirkeby

[email protected]


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