Spatial modelling for low pathogenicity avian influenza virus at the interface of wild birds and backyard poultry

Low pathogenicity avian influenza virus (LPAIV) is endemic in wild birds and poultry in Argentina, and active surveillance has been in place to prevent any eventual virus mutation into a highly pathogenic avian influenza virus (HPAIV), which is exotic in this country. Risk mapping can contribute eff...

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Bibliographic Details
Published in:Transboundary and Emerging Diseases
Main Authors: la Sala, Luciano Francisco, Burgos, Julian Mariano, Blanco, Daniel Ezequiel Roberto, Stevens, Kim B., Fernández, Andrea R., Capobianco, Guillermo, Tohmé, Fernando Abel, Perez, Andres Maximiliano
Format: Article in Journal/Newspaper
Language:English
Published: Wiley Blackwell Publishing, Inc
Subjects:
Online Access:http://hdl.handle.net/11336/92814
Description
Summary:Low pathogenicity avian influenza virus (LPAIV) is endemic in wild birds and poultry in Argentina, and active surveillance has been in place to prevent any eventual virus mutation into a highly pathogenic avian influenza virus (HPAIV), which is exotic in this country. Risk mapping can contribute effectively to disease surveillance and control systems, but it has proven a very challenging task in the absence of disease data. We used a combination of expert opinion elicitation, multicriteria decision analysis (MCDA) and ecological niche modelling (ENM) to identify the most suitable areas for the occurrence of LPAIV at the interface between backyard domestic poultry and wild birds in Argentina. This was achieved by calculating a spatially explicit risk index. As evidenced by the validation and sensitivity analyses, our model was successful in identifying high-risk areas for LPAIV occurrence. Also, we show that the risk for virus occurrence is significantly higher in areas closer to commercial poultry farms. Although the active surveillance systems have been successful in detecting LPAIV-positive backyard farms and wild birds in Argentina, our predictions suggest that surveillance efforts in those compartments could be improved by including high-risk areas identified by our model. Our research provides a tool to guide surveillance activities in the future, and presents a mixed methodological approach which could be implemented in areas where the disease is exotic or rare and a knowledge-driven modelling method is necessary. Fil: la Sala, Luciano Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias Biológicas y Biomédicas del Sur. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Instituto de Ciencias Biológicas y Biomédicas del Sur; Argentina Fil: Burgos, Julian Mariano. Marine and Freshwater Research Institute; Islandia Fil: Blanco, Daniel Ezequiel Roberto. Wetlands International. ...