A Systems Dynamics Model to Mitigate the Risk of Contaminated Feed in Egg Production Systems in the USA

Olatoye I. Olufemi, Olagoke Ayeni and Olasumbo Esther Olagoke-Komolafe
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Olatoye I. Olufemi: Center for Food Safety and Public Health, Lexington KY USA Department of Biology, Morgan State University, Baltimore MD, USA
Olagoke Ayeni: Independent Researcher, Nigeria
Olasumbo Esther Olagoke-Komolafe: Sweet Sensation Confectionery Limited, Nigeria

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 1, 1756-1771

Abstract: Contaminated feed poses significant risks to egg production systems in the United States, serving as a primary vector for introducing pathogens such as Salmonella and E. coli, as well as mycotoxins. These contaminants threaten public health, reduce flock productivity, and impose substantial economic losses due to recalls and regulatory penalties. This review proposes a systems dynamics model to assess and mitigate the risks of contaminated feed within U.S. egg production systems. The framework integrates dynamic simulation techniques to analyze contamination pathways, predict outbreaks, and evaluate intervention strategies across feed sourcing, processing, storage, and distribution stages. Key components of the model include feedback loops that capture interactions between contamination levels, detection methods, and mitigation actions. Predictive analytics and scenario testing allow stakeholders to optimize preventive measures, including supplier audits, thermal treatment, and chemical decontamination processes. Real-time monitoring systems, enhanced by the Internet of Things (IoT) and blockchain technology, are incorporated to improve traceability and ensure rapid response to contamination events. The proposed model also aligns with regulatory standards under the Food Safety Modernization Act (FSMA), providing a structured approach to compliance and risk management. Sensitivity analysis validates the model’s robustness, highlighting its ability to adapt to variations in feed quality and production scales. Results indicate that integrating dynamic systems modeling with technological solutions can significantly reduce contamination rates, improve economic performance, and enhance food safety. Future directions emphasize artificial intelligence (AI)-driven adaptive systems, cross-industry collaboration, and data-sharing platforms to further refine predictive capabilities. This highlights the transformative potential of systems dynamics modeling as a decision-making tool, enabling egg producers to proactively safeguard feed quality and protect public health while ensuring regulatory compliance and economic sustainability.

Date: 2025
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