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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. 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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  1. Bayesian spatial modelling of disease vector data on Danish farmland Carsten Kirkeby Gerard Heuvelink Anders Stockmarr René Bødker

  2. 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

  3. Bluetongue virus • Midge-borne • Infects ruminants • Northern Europe: 2006-2010 • Symptoms: Fever, diarrhoea, reduced milk production Institute of Animal Health UK

  4. Schmallenberg virus • Midge-borne • Infects ruminants • Northern Europe: 2011 - ? • Symptoms: Fever, stillbirths, malformations, reduced milk production Institute of Animal Health UK

  5. 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

  6. Field study x

  7. Field study

  8. Field study

  9. Data

  10. Analysis Count data

  11. Analysis Spatial component “Your neighbours influence you, but you also influence your neighbours.” Charles Manski

  12. Analysis Temporal component t t-1

  13. Analysis R: geoRglm package – GLGM kriging pois.krige.bayes() Bayesian kriging for the poisson spatial model Y ~ β + S(ρ) + ε β = + + + + dayeffect + lag1

  14. Analysis Spatial correlation: Matérn covariance function Φ

  15. Analysis - separate

  16. Analysis - simultaneous

  17. Analysis - simultaneous

  18. Analysis - comparison -0.12 -0.33 Non-spatial Poisson regression 0.07 0.008

  19. Analysis - prediction 1 km

  20. Analysis – temporal covariates

  21. 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

  22. Future • Jackknife • Validation on other dataset

  23. Acknowledgements • Thanks: • Ole Fredslund Christensen • Astrid Blok van Witteloostuijn

  24. Thank you for your attention Carsten Kirkeby ckir@vet.dtu.dk

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