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

Escobar LE, Peterson AT, Papeş M, Favi M, Yung V, Restif O, Qiao H, Medina-Vogel G - Vet. Res. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Facultad de Ecología y Recursos Naturales, Universidad Andres Bello, Av. República 440, Santiago, Chile. ecoguate2003@gmail.com.

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.

No MeSH data available.


Related in: MedlinePlus

Study areas for model calibration and validation (M). Left: geographic position of Chile (dark gray). Right: study areas within central Chile (dark gray) and occurrence points (white squares) in the calibration area (dashed line).
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Fig2: Study areas for model calibration and validation (M). Left: geographic position of Chile (dark gray). Right: study areas within central Chile (dark gray) and occurrence points (white squares) in the calibration area (dashed line).

Mentions: Considering the critical role of the extent of the area of analysis in ENM performance [16], we limited the area for model calibration based on biogeographic barriers that included the Andes mountains (East), Pacific Ocean (West), ice fields in Patagonia (South), and the Atacama Desert (North; Figure 2). This area contained central Chile (43.5° S - 28.0° S) and was our a priori hypothesis regarding the extent of the accessible area, or M [16,17].Figure 2


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

Escobar LE, Peterson AT, Papeş M, Favi M, Yung V, Restif O, Qiao H, Medina-Vogel G - Vet. Res. (2015)

Study areas for model calibration and validation (M). Left: geographic position of Chile (dark gray). Right: study areas within central Chile (dark gray) and occurrence points (white squares) in the calibration area (dashed line).
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4558958&req=5

Fig2: Study areas for model calibration and validation (M). Left: geographic position of Chile (dark gray). Right: study areas within central Chile (dark gray) and occurrence points (white squares) in the calibration area (dashed line).
Mentions: Considering the critical role of the extent of the area of analysis in ENM performance [16], we limited the area for model calibration based on biogeographic barriers that included the Andes mountains (East), Pacific Ocean (West), ice fields in Patagonia (South), and the Atacama Desert (North; Figure 2). This area contained central Chile (43.5° S - 28.0° S) and was our a priori hypothesis regarding the extent of the accessible area, or M [16,17].Figure 2

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Facultad de Ecología y Recursos Naturales, Universidad Andres Bello, Av. República 440, Santiago, Chile. ecoguate2003@gmail.com.

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.

No MeSH data available.


Related in: MedlinePlus