Instituto de Ciencias Biológicas y Biomédicas del Sur (CONICET - Universidad Nacional del Sur), Bahía Blanca, Argentina.
Marine and Freshwater Research Institute, Reykjavík, Iceland.
Transbound Emerg Dis. 2019 Jul;66(4):1493-1505. doi: 10.1111/tbed.13136. Epub 2019 Apr 12.
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.
低致病性禽流感病毒(LPAIV)在阿根廷的野鸟和家禽中流行,为防止病毒最终变异为高致病性禽流感病毒(HPAIV),该国一直进行着积极的监测。风险绘图可以有效地为疾病监测和控制系统做出贡献,但在缺乏疾病数据的情况下,这被证明是一项极具挑战性的任务。我们结合专家意见征询、多准则决策分析(MCDA)和生态位模型(ENM),确定了阿根廷后院家禽和野鸟交界处最适合 LPAIV 发生的区域。这是通过计算空间明确的风险指数来实现的。验证和敏感性分析表明,我们的模型成功地识别了 LPAIV 发生的高风险区域。此外,我们还表明,病毒发生的风险在靠近商业家禽养殖场的区域显著更高。尽管主动监测系统成功地检测到了阿根廷的 LPAIV 阳性后院农场和野鸟,但我们的预测表明,通过包括我们模型确定的高风险区域,可以改善这些区域的监测工作。我们的研究为未来的监测活动提供了一个工具,并提出了一种混合方法,该方法可以在疾病为外来或罕见且需要知识驱动建模方法的地区实施。