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Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning

Cuéllar A.C., Kjaer L.J., Baum A., Stockmarr A., Skovgard H., Nielsen S.A., Andersson M.G., Lindstrom A., Chirico J., Lühken R., Steinke S., Kiel E., Gethmann J.M., Conraths F.J., Larska M., Smreczak M., Orlowska A., Hammes I., Sviland S., Hopp P., Brugger K., Rubel F., Balenghien T., Garros C., Rakotoarivony I., Allene X., Lhoir J., Chavernac D., Delecolle D., Mathieu B., Delecolle D., Setier Rio M.L., Scheid B., Miranda Chueca M.Á., Barcelo C., Lucientes J., Estrada R., Mathis A., Venail R., Tack W., Bodker R.. 2020. Parasites and Vectors, 13 : 18 p..

DOI: 10.1186/s13071-020-04053-x

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.

Mots-clés : culicoides; transmission des maladies; virus bluetongue; virus peste équine africaine; système d'aide à la décision; apprentissage machine; technique de prévision; distribution des populations; modèle mathématique; france; espagne; allemagne; suisse; autriche; pologne; danemark; suède; norvège; schmallenberg virus

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