1
Facultad de Ecología y Recursos Naturales, Universidad Andres Bello, Av.
República 440, Santiago, Chile
2 Center for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, New York, USA
3 Biodiversity Institute, University of Kansas, Lawrence, Kansas, USA
4 Department of Integrative Biology, Oklahoma State University, Stillwater 74078, Oklahoma, USA
5 Sección Rabia, Instituto de Salud Publica de Chile, Av. Maraton 1000, Ñuñoa, Chile
6 Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
7 Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Science, Beijing, China
2 Center for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, New York, USA
3 Biodiversity Institute, University of Kansas, Lawrence, Kansas, USA
4 Department of Integrative Biology, Oklahoma State University, Stillwater 74078, Oklahoma, USA
5 Sección Rabia, Instituto de Salud Publica de Chile, Av. Maraton 1000, Ñuñoa, Chile
6 Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
7 Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Science, Beijing, China
Veterinary Research 2015, 46:92
doi:10.1186/s13567-015-0235-7
The electronic version of this article is the complete one and can be found online at: http://www.veterinaryresearch.org/content/46/1/92
The electronic version of this article is the complete one and can be found online at: http://www.veterinaryresearch.org/content/46/1/92
| Received: | 20 February 2015 |
| Accepted: | 10 August 2015 |
| Published: | 4 September 2015 |
© 2015 Escobar et al.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Abstract
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.
