ArticleParasites & vectors2020
Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning.
Article in Parasites & vectors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 42 citations in OpenAlex.
- Environmental factors influencing Culicoides vectors of Bluetongue in northwestern Spain: abundance, phenology and epidemiological risk.Parasites & vectors · 2026Article
- Quantifying vector diversion effects in zoonotic systems: A modelling framework for arbovirus transmission between reservoir and dead-end hosts.PLoS computational biology · 2025Article
- Spatio-temporal dataset (2009-2012) of Culicoides spp., vectors of livestock viruses, in France.Scientific data · 2025Article
- Emergence of Autochthonous Leishmania (Mundinia) martiniquensis Infections in Horses, Czech Republic and Austria, 2019-2023.Emerging infectious diseases · 2025Article
- Emergence and evolution of epizootic hemorrhagic disease virus in the Mediterranean region: spatio-temporal dynamics and epidemiological insights.Frontiers in veterinary science · 2025Article
- Delayed post-juvenile moult in malaria-infected Eurasian blackcaps.Proceedings. Biological sciences · 2025Article
- Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.Parasites & vectors · 2024Article
- Sampling of Culicoides with nontraditional methods provides unusual species composition and new records for southern Spain.Parasites & vectors · 2024Article
- Epidemiological Analyses of the First Incursion of the Epizootic Hemorrhagic Disease Virus Serotype 8 in Tunisia, 2021-2022.Viruses · 2024Article
- VectorNet: collaborative mapping of arthropod disease vectors in Europe and surrounding areas since 2010.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023Article
- WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.International journal of environmental research and public health · 2023Article
- Differential effects of environmental climatic variables on parasite abundances in blue tit nests during a decade.Integrative zoology · 2022Review
- Culicoides species community composition and feeding preferences in two aquatic ecosystems in northern Spain.Parasites & vectors · 2022Article
- Spatio-temporal modelling of Culicoides Latreille (Diptera: Ceratopogonidae) populations on Reunion Island (Indian Ocean).Parasites & vectors · 2021Article
- High dispersal capacity of Culicoides obsoletus (Diptera: Ceratopogonidae), vector of bluetongue and Schmallenberg viruses, revealed by landscape genetic analyses.Parasites & vectors · 2021Article
- Thermal limits for flight activity of field-collected Culicoides in the United Kingdom defined under laboratory conditions.Parasites & vectors · 2021Article
- Article
- Role of eco-climatic factors in the distribution of bluetongue in endemic areas in Tunisia.Open veterinary journalArticle
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Authors and funding
41 authors at 20 institutions in 11 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCulicoides 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.
methodsWe 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 km
resultsThe 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.
conclusionsThe 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 R
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