Development of an early-warning system based on real-time risk assessment for the prevention and rapid control of Avian Influenza in California Poultry industry

The recent cases of highly pathogenic avian influenza (HPAI) in a commertial Turkey flock in Stanislaus country (H5N8, Jan 2015) and a commertial poultry flock (broiler chickens and ducks) in Kings county (Academic SenateH5N8, Feb 2015) highlights the urgent need to develop and implement solutions to protect California poultry operations (PO) against avian influenza (AI) outbreaks. The unique peculiarities of the different types of PO coexisting in California (CA) (i.e., organic vs commercial, backyard flocks, live bird markets, etc.) pose a challenge on the early detection and control of diseases such as AI which cost producers and the US millions of dollars. Mapping the occurrence of AI in wild birds and the presence of environmental and anthropogenic factors for AI occurrence has been proven useful to identify high-risk areas for poultry exposure to AI virus in countries such as China or Thailand (Gilbert et al., 2008a; Fang et al., 2013a; Gilbert et al., 2014); however, the awareness of the producers and the implementation of appropriate biosecurity and management practices on farm are key to prevent and mitigate the consequences of an AI outbreak. The aim of this project is to pilot the development an innovative early-warning system based on scientific-based risk maps, real-time notifications, on-farm risk assessments and educational tools for better prevention and control of AI outbreaks in CA. First, we will generate high-resolution AI risk maps and identify environmental, climatic and anthropogenic factors associated with AI occurrence in CA using maximum entropy ecological niche modeling. Those methods will be integrated into a web-based, dynamic, platform with capabilities to send automatic notifications to producers if changes of AI risk are detected at local or state level. Second, a self-assessment tool will allow producers to quantify the specific risk of AI virus exposure in their operations at any time given their specific location, biosecurity and management practices. Finally, we will implement workshops to increase awareness, training and responsiveness of both small-scale and large-scale producers about biosecurity practices and early detection of AI. Results of this project will built capacity, increase awareness and provide updated risk-base estimates to better prevent, detect and control AI outbreaks in CA.

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