Journal article
Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning
National Veterinary Institute, Technical University of Denmark1
National Veterinary Research Institute2
Norwegian Veterinary Institute3
University of Veterinary Medicine Vienna4
Institut agronomique et vétérinaire Hassan II5
Centre de coopération internationale en recherche agronomique pour le développement6
Université de Strasbourg7
EID Méditerranée8
University of the Balearic Islands9
University of Zaragoza10
University of Zurich11
Department of Applied Mathematics and Computer Science, Technical University of Denmark12
Avia-GIS NV13
Botanic Garden Meise14
Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark15
Aarhus University16
Roskilde University17
National Veterinary Institute18
Bernhard Nocht Institute for Tropical Medicine19
University of Oldenburg20
Friedrich-Loeffler-Institute21
...and 11 moreBACKGROUND: 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.
Language: | English |
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Publisher: | BioMed Central |
Year: | 2020 |
Pages: | 194 |
ISSN: | 17563305 |
Types: | Journal article |
DOI: | 10.1186/s13071-020-04053-x |
ORCIDs: | Baum, Andreas and Stockmarr, Anders |