Development of an early-warning system based on real-time risk assessment, producers self-assessment of biosecurity & educational tools for the prevention, early detection & rapid control of AI outbreaks in the US poultry industry
The goal of this interdisciplinary, multi-institution, research-extension project within the "Critical Agricultural Research and Extension (CARE)" priority is to develop an innovative early-warning system for better prevention and control of Avian Influenza (AI) outbreaks in US poultry industry. The specific objectives are: i) produce accurate, continuosly updated, high-resolution AI risk maps and identify key factors (e.g., environmental, climatic and anthropogenic factors) associated with AI occurrence in US, ii) integrate those risk maps into a web-based platform for easy visualization and with capabilities to send automatic notifications to producers if changes of AI risk are detected at local or regional level, iii) develop a self-assessment tool where producers can quantify the risk of AIV exposure for their operations at any time given their specific location, biosecurity and management practices, iv) design, implement and test the value of outreach activities, workshops and interactive educational tools to increase awareness, training and responsiveness of small-scale and large scale producers about biosecurity practices and early detection of AI. To accomplish those goals high resolution risk maps will be produced using the cutting-edge method of maximum entropy ecological niche modeling. Data and methods will be integrated into a user-friendly web-based and mobile “app” interface to facilitate the long-term access, visualization, analysis and communication of the AI risk to producers and to provide customized recommendations and educational tools for implementing risk-mitigation measures. This work will provide valuable knowledge and operational tools for poultry producers and other stakeholders to better prevent and control AI outbreaks in the US.
SOW: Web-based System for Surveillance of Infectious Animal Disease in the European Union
Goal: To develop a web-based system for surveillance of infectious animal disease in Europe
Specific objectives
1. To collaborate with AHVLA staff on maintaining the utility of Bioportal for application to transboundary or notifiable disease events in poultry and wild birds in real time including exercises with data supplied by AHVLA.
2. To respond on an emergency basis to support the EURL and Member States in the event of an avian influenza outbreak.
3. The specific objectives, scope, progress and contributions of the work by CADMS staff to be discussed and agreed at monthly teleconferences with AHVLA.
To contribute to training and development opportunities of EU National Reference Laboratory (NRL) staff and EURL staff as agreed with AHVLA.
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 (H5N8, 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.
The goal of this interdisciplinary, multi-institution, research-extension project within the "Critical Agricultural Research and Extension (CARE)" priority is to develop an innovative early-warning system for better prevention and control of Avian Influenza (AI) outbreaks in US poultry industry. The specific objectives are: i) produce accurate, continuosly updated, high-resolution AI risk maps and identify key factors (e.g., environmental, climatic and anthropogenic factors) associated with AI occurrence in US, ii) integrate those risk maps into a web-based platform for easy visualization and with capabilities to send automatic notifications to producers if changes of AI risk are detected at local or regional level, iii) develop a self-assessment tool where producers can quantify the risk of AIV exposure for their operations at any time given their specific location, biosecurity and management practices, iv) 
design, implement and test the value of outreach activities, workshops and interactive educational tools to increase awareness, training and responsiveness of small-scale and large scale producers about biosecurity practices and early detection of AI. To accomplish those goals high resolution risk maps will be produced using the cutting-edge method of maximum entropy ecological niche modeling. Data and methods will be integrated into a user-friendly web-based and mobile “app” interface to facilitate the long-term access, visualization, analysis and communication of the AI risk to producers and to provide customized recommendations and educational tools for implementing risk-mitigation measures. This work will provide valuable knowledge and operational tools for poultry producers and other stakeholders to better prevent and control AI outbreaks in the US.
Goal: To develop a web-based system for surveillance of infectious animal disease in Europe
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 (H5N8, 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.