Ståle Sviland, Franz Josef Conraths, Magdalena Larska, Miguel Ángel Miranda Chueca, Alexander Mathis, Rene Bødker, Henrik Skovgård, Claire Garros, Thomas Balenghien, Renke Lühken, David Chavernac, Ignace Rakotoarivony, Delphine Delecolle, Bethsabée Scheid, Xavier Allene, Jan Chirico, Sonja Steinke, Petter Hopp, Marie-Laure Setier-Rio, Javier Lucientes, Ellen Kiel, Franz Rubel, Mats Gunnar Andersson, Bruno Mathieu, Anders Lindström, Anders Stockmarr, Anna Orłowska, Inger Sofie Hamnes, R. Estrada, Jörn Gethmann, Roger Venail, Wesley Tack, Katharina Brugger, Carlos Barceló, Ana Carolina Cuellar, Jonathan Lhoir, Søren Nielsen, Lene Jung Kjær, Andreas Baum, Marcin Smreczak, Jean-Claude Delécolle, Technical University of Denmark [Lyngby] (DTU), Aarhus University [Aarhus], Roskilde Universitet [Roskilde], National Veterinary Institute, University of Hamburg, Bernhard Nocht Institute for Tropical Medicine - Bernhard-Nocht-Institut für Tropenmedizin [Hamburg, Germany] (BNITM), University of Oldenburg, Friedrich-Loeffler-Institut (FLI), National Veterinary Research Institute [Pulawy, Pologne] (NVRI), Norwegian Veterinary Institute [Oslo], University of Veterinary Medicine, Vienna, Austria, Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University Hassan II [Casablanca], Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut de Parasitologie et de Pathologie Tropicale (IPPTS), Université de Strasbourg (UNISTRA), Entente interdépartementale pour la démoustication du littoral méditerranéen [Montpellier] (EID Méditerranée), EID-Méditerranée, University of the Balearic Islands (UIB), University of Zaragoza - Universidad de Zaragoza [Zaragoza], Universität Zürich [Zürich] = University of Zurich (UZH), Entente Interdépartementale pour la Démoustication du Littoral Méditerranéen, Avia-GIS [Zoersel], Botanic Garden Meise, and This study was funded by the EMIDA ERA-NET-supported project VICE (Vector-borne Infections: Risk-based and Cost-Effective Surveillance Systems). Culicoides data from Germany were partly collected within the German part of the VICE project funded by EMIDA ERA-NET through the Federal Office for Agriculture and Food (grant no. 314-06.01-2811ERA248).
Background Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe. Methods We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km2 resolution. We used independent test sets for validation and to assess model performance. Results The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish differences in abundance between countries but was not able to predict vector abundance at individual farm level. Conclusions The models and maps presented here represent an initial attempt to capture large scale geographical and temporal variations in Culicoides abundance. The models are a first step towards producing abundance inputs for R0 modelling of Culicoides-borne infections at a continental scale.