Ecological approaches in veterinary epidemiology: mapping the risk of bat-borne rabies using vegetation indices and night-time light satellite imagery

Springer Science and Business Media LLC - Tập 46 - Trang 1-10 - 2015
Luis E Escobar1,2, A Townsend Peterson3, Monica Papeş4, Myriam Favi5, Veronica Yung5, Olivier Restif6, Huijie Qiao7, Gonzalo Medina-Vogel1
1Facultad de Ecología y Recursos Naturales, Universidad Andres Bello, Santiago, Chile
2Center for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, USA
3Biodiversity Institute, University of Kansas, Lawrence, USA
4Department of Integrative Biology, Oklahoma State University, Stillwater, USA
5Sección Rabia, Instituto de Salud Publica de Chile, Ñuñoa, Chile
6Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
7Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Science, Beijing, China

Tóm tắt

Rabies remains a disease of significant public health concern. In the Americas, bats are an important source of rabies for pets, livestock, and humans. For effective rabies control and prevention, identifying potential areas for disease occurrence is critical to guide future research, inform public health policies, and design interventions. To anticipate zoonotic infectious diseases distribution at coarse scale, veterinary epidemiology needs to advance via exploring current geographic ecology tools and data using a biological approach. We analyzed bat-borne rabies reports in Chile from 2002 to 2012 to establish associations between rabies occurrence and environmental factors to generate an ecological niche model (ENM). The main rabies reservoir in Chile is the bat species Tadarida brasiliensis; we mapped 726 occurrences of rabies virus variant AgV4 in this bat species and integrated them with contemporary Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The correct prediction of areas with rabies in bats and the reliable anticipation of human rabies in our study illustrate the usefulness of ENM for mapping rabies and other zoonotic pathogens. Additionally, we highlight critical issues with selection of environmental variables, methods for model validation, and consideration of sampling bias. Indeed, models with weak or incorrect validation approaches should be interpreted with caution. In conclusion, ecological niche modeling applications for mapping disease risk at coarse geographic scales have a promising future, especially with refinement and enrichment of models with additional information, such as night-time light data, which increased substantially the model’s ability to anticipate human rabies.

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