AI for Agricultural Resilience: Modeling the Economic Impact of Avian Influenza Outbreaks in the U.S.

Avian influenza continues to pose a significant threat to the U.S. poultry sector, with recurring outbreaks inflicting substantial economic losses and challenging existing biosecurity infrastructures. This study explores the application of artificial intelligence (AI) and machine learning (ML) to simulate the economic impacts of avian influenza outbreaks across different U.S. regions and poultry production systems. It integrates epidemiological data, trade and production statistics, and hypothetical IoT sensor inputs into a multi-layered simulation framework. The study models scenario-based economic outcomes under varying intervention timings, vaccine coverage rates, and trade policy responses. Employing Random Forest algorithms and Monte Carlo simulations, the model forecasts sector-specific losses in production, supply chain disruptions, labor productivity, insurance claims, and export revenues. Scenario analyses reveal that predictive analytics, when applied through proactive interventions, offer strategic advantages in outbreak containment and resource optimization. The findings highlight AI’s transformative role in enhancing agricultural resilience by enabling anticipatory decision-making, dynamic risk assessment, and data-driven policy formulation. The study advocates for the integration of AI-powered scenario modeling into the USDA’s emergency planning frameworks and emphasizes the need for stronger public-private partnerships and real-time data ecosystems. In an era of escalating zoonotic threats, such AI-driven approaches are pivotal for safeguarding U.S. food security, economic stability, and global trade competitiveness.