Allene, X.

Anderson, G.

Balenghien, T.

Barcelo Segui, C.

Brugger, K.

Bødker, Rene

Chirico, J.

Cuellar, Ana Carolina

Delecolle, D.

Delecolle, J. C.

Estrada, R.

Garros, C.

Hamnes, S. I.

Hopp, Petter

Kiel, E.

Kjær, Lene Jung

Larska, Magdalena

Lhoir, J.

Lilja, T.

Lindström, Anders

Lucientes, J.

Lühken, R.

Mathieu, B.

Mathis, A.

Miranda Chueca, M. A.

Nielsen, Søren Archim

Rakotoarivony, I.

Rubel, F.

Scheid, B.

Setier-Rio, M. L.

Skovgaard, Henrik

Steinke, S.

Stockmarr, Anders

Sviland, S.

Venail, R.

Wesley, Tack

Biting midges (Diptera, Ceratopogonidae) of the genus Culicoides are important vectors of pathogens causing diseases in free living and production animals and can lead to large economic losses in many European countries. In Europe, Culicoides imicola and the Obsoletus group are considered to be the main vectors of bluetongue virus that mostly affects ruminants such as cattle and sheep. Spatio-temporal modelling of vector distribution and abundance allows us to identify high risk areas for virus transmission and can aid in applying effective surveillance and control measures. We used presence-absence and monthly abundance data of Culicoides from 1005 sites across 9 countries (Spain, France, Denmark, Poland, Switzerland, Austria, Poland, Sweden, Norway) collected between the years 2007 and 2013. The dataset included information on the vector species abundance (number of specimens caught per night), GPS coordinates of each trap, start and end dates of trapping. We used 120 environmental predictor variables together with Random Forest machine learning algorithms to predict the overall species distribution (probability of occurrence) and monthly abundance in Europe. We generated maps for every month of the year, to visualize the abundance of C. imicola and Obsoletus group in Europe as well as distribution maps showing the probability of occurrence. We were able to create predictive maps of both Culicoides sp. occurrence and abundance using Random Forest models, and although the variance was large, the predicted abundance values for each site had a positive correlation with the observed abundance. We found relatively large spatial variations in probability of occurrence and abundance for both C. imicola and the Obsoletus group. For C. imicola probability of occurrence and abundance was higher in southern Spain, where as the Obsoletus group had higher probability of occurrence and abundance in central and northern Europe such as France and Germany. Temporal variation was also observed with higher abundance occurring during summer months and low or no abundance during winter months for both C. imicula and the Obsoletus group, although abundance was generally higher for a longer period of time for C. imicula than for the Obsoletus group.Using machine learning techniques, we were able to model the spatial distribution in Europe for C. imicola and the Obsoletus group in terms of abundance and suitability (probability of occurrence). Our maps corresponded well with the previously reported distribution for C. imicola and the Obsoletus group. The observed seasonal variation was also consistent with reported population dynamics for Culicoides, as it depends on environmental factors such as temperature and rainfall. Longer seasonal abundance for C. imicula compared to the Obsoletus group can be explained by the species distribution, as C. imicula is limited to the southern parts of Europe where the warm season lasts longer, whereas the Obsoletus group is found further north. The outputs obtained here will be used as input for epidemiological models and can be helpful for determining high risk areas for disease transmission.<br/

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Downloaded from orbit.dtu.dk on: Dec 18, 2017Big data - modelling of midges in Europa using machine learning techniques andsatellite imageryCuellar, Ana Carolina; Kjær, Lene Jung; Skovgaard, Henrik; Nielsen, Søren Archim; Stockmarr, Anders;Anderson, G.; Lindström, Anders; Chirico, J.; Lilja, T.; Lühken, R.; Steinke, S.; Kiel, E.; Larska,Magdalena; Hamnes, S. I.; Sviland, S.; Hopp, Petter; Brugger, K.; Rubel, F.; Balenghien, T.; Garros, C.;Rakotoarivony, I.; Allene, X.; Lhoir, J.; Delecolle, J. C.; Mathieu, B.; Delecolle, D.; Setier-Rio, M. L.;Venail, R.; Scheid, B.; Miranda Chueca, M. A.; Barcelo Segui, C.; Lucientes, J.; Estrada, R.; Wesley, Tack;Mathis, A.; Bødker, RenePublished in:NKVet Symposium 2017 - abstract bookPublication date:2017Document VersionPeer reviewed versionLink back to DTU OrbitCitation (APA):Cuellar, A. C., Kjær, L. J., Skovgaard, H., Nielsen, S. A., Stockmarr, A., Anderson, G., ... Bødker, R. (2017). Bigdata - modelling of midges in Europa using machine learning techniques and satellite imagery. In NKVetSymposium 2017 - abstract book Oslo, Norway.  BIG DATA - MODELLING OF MIDGES IN EUROPA USING MACHINE LEARNING TECHNIQUES AND SATELLITE IMAGERY  Cuellar A.C.1, Kjær, L.J1, Skovgaard H. 2, Nielsen S.A.3, Stockmarr A.4, Anderson G.5, Lindstrom A. 5, Chirico J. 5, Lilja T.5, Lühken R. 6, Steinke S. 6, Kiel E.6, Larska M.7, Hamnes S.I.8, Sviland S. 8, Hopp P. 8, Brugger K.9, Rubel F.9, Balenghien T.10, Garros C. 10, Rakotoarivony I.10, Allène X. 10, Lhoir J. 10, Delécolle J.C. 11, Mathieu B. 11, Delécolle D. 11, Setier-Rio M.L. 12, Venail R. 12, Scheid B. 12, Miranda Chueca M.A. 13, Barcelo Segui C. 13, Lucientes J.14, Estrada R.14, Wesley Tack15, Mathis A.16,  and Bødker R.1 1Section for Diagnostics and Scientific Advice, Technical University of Denmark (DTU), National Veterinary Institute, Copenhagen, Denmark  2 Department of Agroecology - Entomology and Plant Pathology, Aarhus University, Aarhus, Denmark 3 Institute for Science and Environment, Roskilde University, Roskilde, Denmark 4Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Copenhagen, Denmark 5National Veterinary Institute (SVA), Uppsala, Sweden 6Bernhard Nocht Institute for Tropical Medicine, WHO Collaborating Centre for Arbovirus and Haemorrhagic Fever Reference and Research National Reference Centre for Tropical Infectious Diseases, Hamburg, Germany 7Department of Virology, National Veterinary Research Institute, Pulawy, Poland 8Norwegian Veterinary Institute, Oslo, Norway 9 Institute for Veterinary Public Health, Vienna, Austria 10Agricultural research for development (CIRAD), Montpellier, France 11Institute of parasitology and tropical diseases of Strasbourg, Université de Strasbourg, Strasbourg, France 12 EID Méditerranée, Montpellier, France 13 Laboratory of Zoology, University of the Balearic Islands, Spain 14 Department of Animal Pathology, University of Zaragoza, Spain 15Avia- GIS, Zoersel, Belgium 16 Institute of Parasitology, University of Zürich, Zürich, Switzerland Corresponding author: anacu@vet.dtu.dk  Biting midges (Diptera, Ceratopogonidae) of the genus Culicoides are important vectors of pathogens causing diseases in free living and production animals and can lead to large economic losses in many European countries. In Europe, Culicoides imicola and the Obsoletus group are considered to be the main vectors of bluetongue virus that mostly affects ruminants such as cattle and sheep. Spatio-temporal modelling of vector distribution and abundance allows us to identify high risk areas for virus transmission and can aid in applying effective surveillance and control measures.    We used presence-absence and monthly abundance data of Culicoides from 1005 sites across 9 countries (Spain, France, Denmark, Poland, Switzerland, Austria, Poland, Sweden, Norway) collected between the years 2007 and 2013. The dataset included information on the vector species abundance (number of specimens caught per night), GPS coordinates of each trap, start and end dates of trapping. We used 120 environmental predictor variables together with Random Forest machine learning algorithms to predict the overall species distribution (probability of occurrence) and monthly abundance in Europe. We generated maps for every month of the year, to visualize the abundance of C. imicola and Obsoletus group in Europe as well as distribution maps showing the probability of occurrence.  We were able to create predictive maps of both Culicoides sp. occurrence and abundance using Random Forest models, and although the variance was large, the predicted abundance values for each site had a positive correlation with the observed abundance. We found relatively large spatial variations in probability of occurrence and abundance for both C. imicola and the Obsoletus group. For C. imicola probability of occurrence and abundance was higher in southern Spain, where as the Obsoletus group had higher probability of occurrence and abundance in central and northern Europe such as France and Germany. Temporal variation was also observed with higher abundance occurring during summer months and low or no abundance during winter months for both C. imicula and the Obsoletus group, although abundance was generally higher for a longer period of time for C. imicula than for the Obsoletus group. Using machine learning techniques, we were able to model the spatial distribution in Europe for C. imicola and the Obsoletus group in terms of abundance and suitability (probability of occurrence). Our maps corresponded well with the previously reported distribution for C. imicola and the Obsoletus group. The observed seasonal variation was also consistent with reported population dynamics for Culicoides, as it depends on environmental factors such as temperature and rainfall. Longer seasonal abundance for C. imicula compared to the Obsoletus group can be explained by the species distribution, as C. imicula is limited to the southern parts of Europe where the warm season lasts longer, whereas the Obsoletus group is found further north. The outputs obtained here will be used as input for epidemiological models and can be helpful for determining high risk areas for disease transmission. 

Big data - modelling of midges in Europa using machine learning techniques and satellite imagery

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Big data - modelling of midges in Europa using machine learning techniques and satellite imagery

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