Fachbereich Veterinärmedizin



    Modelling the spatial distribution of Fasciola hepatica in dairy cattle in Europe (2015)

    Zeitschriftenartikel / wissenschaftlicher Beitrag
    Ducheyne, Els
    Charlier, Johannes
    Vercruysse, Jozef
    Rinaldi, Laura
    Biggeri, Annibale
    Demeler, Janina (WE 13)
    Brandt, Christina (WE 13)
    De Waal, Theo
    Selemetas, Nikolaos
    Höglund, Johan
    Kaba, Jaroslaw
    Kowalczyk, Slawomir J
    Hendrickx, Guy
    Geospatial health; 9(2) — S. 261–270
    ISSN: 1827-1987
    DOI: 10.4081/gh.2015.348
    Pubmed: 25826307
    Institut für Parasitologie und Tropenveterinärmedizin

    Robert-von-Ostertag-Str. 7-13
    Gebäude 35, 22, 23
    14163 Berlin
    +49 30 838 62310

    Abstract / Zusammenfassung

    A harmonized sampling approach in combination with spatial modelling is required to update current knowledge of fasciolosis in dairy cattle in Europe. Within the scope of the EU project GLOWORM, samples from 3,359 randomly selected farms in 849 municipalities in Belgium, Germany, Ireland, Poland and Sweden were collected and their infection status assessed using an indirect bulk tank milk (BTM) enzyme-linked immunosorbent assay (ELISA). Dairy farms were considered exposed when the optical density ratio (ODR) exceeded the 0.3 cut-off. Two ensemble-modelling techniques, Random Forests (RF) and Boosted Regression Trees (BRT), were used to obtain the spatial distribution of the probability of exposure to Fasciola hepatica using remotely sensed environmental variables (1-km spatial resolution) and interpolated values from meteorological stations as predictors. The median ODRs amounted to 0.31, 0.12, 0.54, 0.25 and 0.44 for Belgium, Germany, Ireland, Poland and southern Sweden, respectively. Using the 0.3 threshold, 571 municipalities were categorized as positive and 429 as negative. RF was seen as capable of predicting the spatial distribution of exposure with an area under the receiver operation characteristic (ROC) curve (AUC) of 0.83 (0.96 for BRT). Both models identified rainfall and temperature as the most important factors for probability of exposure. Areas of high and low exposure were identified by both models, with BRT better at discriminating between low-probability and high-probability exposure; this model may therefore be more useful in practise. Given a harmonized sampling strategy, it should be possible to generate robust spatial models for fasciolosis in dairy cattle in Europe to be used as input for temporal models and for the detection of deviations in baseline probability. Further research is required for model output in areas outside the eco-climatic range investigated.