Predictive Analytics framework for real-time surveillance of Antimicrobial Resistance in Food systems

Antimicrobial resistance (AMR) poses a looming threat to global health, undermining decades of progress in human and veterinary medicine. In the United States, livestock production accounts for over 70% of medically important antimicrobial use, fueling the emergence of resistant pathogens that can transfer to humans through the food chain and environment. Existing surveillance mechanisms, such as the National Antimicrobial Resistance Monitoring System (NARMS), offer retrospective insights with significant delays, limiting timely intervention. We propose a Predictive Engineering Framework that integrates IoT-enabled farm sensors, veterinary prescription records, and environmental sampling into a centralized real-time surveillance platform. By applying Long Short-Term Memory (LSTM) networks for trend forecasting and Random Forest classification for hotspot detection, our system achieves a 0.7% mean absolute error in 14-day resistance forecasts and 85% classification accuracy for high-risk events. Pilot deployments on ten Midwestern hog farms demonstrated a 22% reduction in antimicrobial use and an 18% decrease in clinical resistance incidents over six months. This framework delivers actionable insights via interactive dashboards and automated alerts, enabling proactive antimicrobial stewardship and rapid outbreak response. National-scale adoption promises to save $75 million annually in livestock antimicrobial expenditures and reduce human healthcare costs by $200 million through early resistance mitigation. We recommend integrating this platform into USDA and CDC surveillance programs to safeguard U.S. food security and public health.