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

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

  • Virus: African Horse Sickness,

  • Akabane Virus etc.

Institute of Animal Health UK


Bluetongue virus

  • Midge-borne

  • Infects ruminants

  • Northern Europe: 2006-2010

  • Symptoms: Fever, diarrhoea, reduced milk production

Institute of Animal Health UK


Schmallenberg virus

  • Midge-borne

  • Infects ruminants

  • Northern Europe: 2011 - ?

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

Institute of Animal Health UK


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

x


Field study


Field study


Data


Analysis

Count data


Analysis

Spatial component

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

Charles Manski


Analysis

Temporal component

t

t-1


Analysis

R: geoRglm package – GLGM kriging

pois.krige.bayes()

Bayesian kriging for the poisson spatial model

Y ~ β + S(ρ) + ε

β = + + + + dayeffect + lag1


Analysis

Spatial correlation: Matérn covariance function

Φ


Analysis - separate


Analysis - simultaneous


Analysis - simultaneous


Analysis - comparison

-0.12

-0.33

Non-spatial

Poisson

regression

0.07

0.008


Analysis - prediction

1 km


Analysis – temporal covariates


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

  • Jackknife

  • Validation on other dataset


Acknowledgements

  • Thanks:

  • Ole Fredslund Christensen

  • Astrid Blok van Witteloostuijn


Thank you for your attention

Carsten Kirkeby

ckir@vet.dtu.dk


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