Backyard flock sampling and artificial intelligence: A dual strategy for early detection of Avian Influenza and Newcastle Disease

Avian Influenza (AI) and Newcastle Disease (ND) are significant avian diseases that pose substantial threats to the commercial poultry industry due to their high contagion rates and potential for severe outcomes. Recent shifts in the epidemiology and ecology of both viruses have led to devastating impacts on wild bird and poultry populations, as well as farms and communities worldwide. These challenges underscore the necessity for One Health approaches to pandemic prevention and preparedness, emphasizing the need for multisectoral collaboration among animal, environmental, and public health sectors. To mitigate future pandemic risks, it is essential to control the transmission of AI and ND viruses among domestic and wild animals as well as humans. This involves addressing the upstream drivers of outbreaks, ensuring rapid response mechanisms, and conducting thorough risk assessments for zoonotic diseases. Effective implementation and maintenance of prevention programs, surveillance, and outbreak responses require strong political commitment and sustainable funding. A robust combination of active and passive surveillance strategies should be applied to monitor and control them. In addition, the integration of modern technologies, such as artificial intelligence and big data analytics, can enhance both active and passive surveillance efforts by improving the accuracy and speed of data collection, analysis, and reporting. The use of backyard flocks as an early warning system for avian diseases represents a proactive approach to understanding and managing the risks associated with AI and ND through targeted surveillance and timely interventions. In response to these challenges, the EpirORNIS Project aimed to leverage these sentinel backyard flocks for early detection of AI and ND, thereby enhancing surveillance and control strategies.