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

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 = + + + + day effect + lag 1

14. Analysis Spatial correlation: Matérn covariance function Φ

15. Analysis - separate

16. Analysis - simultaneous

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

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