A random forest approach for predicting the presence ofEchinococcus multilocularisintermediate hostOchotona spp. presence in relation to landscape characteristics in western China.
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 (Ochotona spp.) small mammal intermediate host for the parasitic tapewormEchinococcus multiloculariswhich is responsible for a significant burden of human zoonoses in western China. Landsat ETM+ satellite 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 ofOchotona spp. presence, enabling the relative importance of the environmental characteristics in relation toOchotona spp. presence to be ranked. An index of habitat aggregation was identified as the most important variable in influencingOchotona spp. presence, with area of degraded grassland the most important land cover class variable. 71% of the variance inOchotona spp. presence was explained, with a 90.98% accuracy rate as determined by 'out-of-bag' error assessment. Identification of the environmental characteristics influencingOchotona 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.
School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool. L3 3AF, UK. c.g.marston@ljmu.ac.uk Tel: +441512312401.
Christopher G
CG
Marston
School of Environment and Life Sciences, University of Salford, Manchester. M5 4WT, UK. f.m.danson@salford.ac.uk , r.p.armitage@salford.ac.uk , p.s.craig@salford.ac.uk.
F Mark
FM
Danson
School of Environment and Life Sciences, University of Salford, Manchester. M5 4WT, UK. f.m.danson@salford.ac.uk , r.p.armitage@salford.ac.uk , p.s.craig@salford.ac.uk.
Richard P
RP
Armitage
Department of Chrono-environment and Institut Universitaire de France, University of Franche-Comté, Place Leclerc, 25030 Besançon cedex, France. patrick.giraudoux@univ-fcomte.fr.
Patrick
P
Giraudoux
INRA, UMR-1351 CMAEE, Domaine Duclos, Prise D'eau, 97122 Petit Bourg, Guadeloupe. David.Pleydell@antilles.inra.fr.
David R J
DR
Pleydell
Sichuan Centers for Disease Control and Prevention, Chengdu 610041, Sichuan, China. wangqian67@gmail.com , qiujiamin45@163.com.
Qian
Q
Wang
Sichuan Centers for Disease Control and Prevention, Chengdu 610041, Sichuan, China. wangqian67@gmail.com , qiujiamin45@163.com.
Jiamin
J
Qui
School of Environment and Life Sciences, University of Salford, Manchester. M5 4WT, UK. f.m.danson@salford.ac.uk , r.p.armitage@salford.ac.uk , p.s.craig@salford.ac.uk.
Philip S
PS
Craig
Echinococcus multilocularis; Ochotona; classification; landscape metrics; random forests; remote sensing