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

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Presentation transcript:

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: Symptoms: Fever, diarrhoea, reduced milk production Institute of Animal Health UK

Schmallenberg virus Midge-borne Infects ruminants Northern Europe: ? 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

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 = day effect + lag 1

Analysis Spatial correlation: Matérn covariance function Φ

Analysis - separate

Analysis - simultaneous

Analysis - comparison Non-spatial Poisson regression

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