A random forest approach for predicting the presence of Echinococcus multilocularis intermediate host Ochotona spp. presence in relation to landscape characteristics in western China
Marston, Christopher | Danson, Mark F | Armitage, Richard P | Giraudoux, Patrick | Pleydell, David R.J. | Wang, Qian | Qiu, Jiamin | Craig, Philip S | University of Liverpool | University of Salford | Laboratoire Chrono-environnement (UMR 6249) (LCE) ; Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC) ; Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC) | Institut universitaire de France (IUF) ; Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.) | Contrôle des maladies animales exotiques et émergentes (UMR CMAEE) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA) | Sichuan Provincial Center for Diseases Control and Prevention ; Sichuan Government | US National Institutes of Health and National Science Foundation (EID TW001565-01 & 05)
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Показать больше [+] Меньше [-]Английский. Understanding distribution patterns of hosts implicated in the transmission of zoonotic disease remains a key goal of parasitology. Here, random forests are employed to model spatial patterns of the presence of the plateau pika (Ochotonaspp.) small mammal intermediate host for the parasitic tapewormEchinococcus multiloculariswhich is responsible for a significant burden of human zoonoses in western China. Landsat ETMþsatellite imagery and digital elevation model data were utilized to generate quantified measures of environmental characteristics across a study area in Sichuan Province, China. Land cover maps were generated identifying the distribution of specific land cover types, with landscape metrics employed to describe the spatial organisation of land cover patches. Random forests were used to model spatial patterns of Ochotona spp. presence, enabling the relative importance of the environmental characteristics in relation to Ochotona spp. presence to be ranked. An index of habitat aggregation was identified as the most important variable in influencing Ochotona spp. presence, with area of degraded grassland the most important land cover class variable. 71% of the variance inOchotonaspp. presence was explained, with a 90.98% accuracy rate as determined by'out-of-bag' error assessment. Identification of the environmental characteristics influencing Ochotona spp. presence enables us to better understand distribution patterns of hosts implicated in the transmission of Em. The predictive mapping of this Em host enables the identification of human populations at increased risk of infection, enabling preventative strategies to be adopted.
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