Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning.
- Cuéllar AC Division for Diagnostics and Scientific Advice, National Veterinary Institute, Technical University of Denmark (DTU), Lyngby, Denmark. anacarocuellar@gmail.com.
- Kjær LJ Division for Diagnostics and Scientific Advice, National Veterinary Institute, Technical University of Denmark (DTU), Lyngby, Denmark.
- Baum A Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Lyngby, Denmark.
- Stockmarr A Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Lyngby, Denmark.
- Skovgard H Department of Agroecology - Entomology and Plant Pathology, Aarhus University, Aarhus, Denmark.
- Nielsen SA Department of Science and Environment, Roskilde University, Roskilde, Denmark.
- Andersson MG National Veterinary Institute (SVA), Uppsala, Sweden.
- Lindström A National Veterinary Institute (SVA), Uppsala, Sweden.
- Chirico J National Veterinary Institute (SVA), Uppsala, Sweden.
- Lühken R Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany.
- Steinke S Department of Biology and Environmental Sciences, Carl von Ossietzky University, Oldenburg, Germany.
- Kiel E Department of Biology and Environmental Sciences, Carl von Ossietzky University, Oldenburg, Germany.
- Gethmann J Institute of Epidemiology, Friedrich-Loeffler-Institut, Greifswald, Germany.
- Conraths FJ Institute of Epidemiology, Friedrich-Loeffler-Institut, Greifswald, Germany.
- Larska M Department of Virology, National Veterinary Research Institute, Pulawy, Poland.
- Smreczak M Department of Virology, National Veterinary Research Institute, Pulawy, Poland.
- Orłowska A Department of Virology, National Veterinary Research Institute, Pulawy, Poland.
- Hamnes I Norwegian Veterinary Institute, Oslo, Norway.
- Sviland S Norwegian Veterinary Institute, Oslo, Norway.
- Hopp P Norwegian Veterinary Institute, Oslo, Norway.
- Brugger K Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine, Vienna, Austria.
- Rubel F Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine, Vienna, Austria.
- Balenghien T CIRAD, UMR ASTRE, 34398, Montpellier, France.
- Garros C IAV Hassan II, Unité MIMC, 10 100, Rabat-Instituts, Morocco.
- Rakotoarivony I IAV Hassan II, Unité MIMC, 10 100, Rabat-Instituts, Morocco.
- Allène X IAV Hassan II, Unité MIMC, 10 100, Rabat-Instituts, Morocco.
- Lhoir J CIRAD, UMR ASTRE, 34398, Montpellier, France.
- Chavernac D CIRAD, UMR ASTRE, 34398, Montpellier, France.
- Delécolle JC Institute of Parasitology and Tropical Pathology of Strasbourg, UR7292, Université de Strasbourg, Strasbourg, France.
- Mathieu B Institute of Parasitology and Tropical Pathology of Strasbourg, UR7292, Université de Strasbourg, Strasbourg, France.
- Delécolle D Institute of Parasitology and Tropical Pathology of Strasbourg, UR7292, Université de Strasbourg, Strasbourg, France.
- Setier-Rio ML EID Méditerranée, Montpellier, France.
- Scheid B EID Méditerranée, Montpellier, France.
- Chueca MÁM Applied Zoology and Animal Conservation Research Group, University of the Balearic Islands, Palma, Spain.
- Barceló C Applied Zoology and Animal Conservation Research Group, University of the Balearic Islands, Palma, Spain.
- Lucientes J Department of Animal Pathology, University of Zaragoza, Zaragoza, Spain.
- Estrada R Department of Animal Pathology, University of Zaragoza, Zaragoza, Spain.
- Mathis A Institute of Parasitology, National Centre for Vector Entomology, Vetsuisse FacultyInstitute of Parasitology, National Centre for Vector Entomology, Vetsuisse Faculty, University of Zürich, Zürich, Switzerland.
- Venail R Avia-GIS NV, Zoersel, Belgium.
- Tack W Meise Botanic Garden, Meise, Belgium.
- Bødker R Division for Diagnostics and Scientific Advice, National Veterinary Institute, Technical University of Denmark (DTU), Lyngby, Denmark.
- 2020-04-17
Published in:
- Parasites & vectors. - 2020
Culicoides abundance
Culicoides seasonality
Environmental variables
Europe
Random Forest machine learning
Spatial predictions
Animals
Ceratopogonidae
Climate
Ecosystem
Europe
Farms
Insect Vectors
Machine Learning
Models, Theoretical
Population Dynamics
Seasons
English
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.
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
- Open access status
- gold
- Identifiers
-
- DOI 10.1186/s13071-020-04053-x
- PMID 32295627
- Persistent URL
- https://folia.unifr.ch/global/documents/48144
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