AI-Driven AMR Surveillance Dashboards for Genomic Epidemiology and Public Health Intelligence

• Understand the fundamentals of antimicrobial resistance and genomic epidemiology.
• Learn how AMR surveillance data is collected, cleaned, and interpreted.
• Explore resistance genes, antibiotic classes, pathogen profiles, and metadata fields.
• Understand how AI can support AMR pattern detection and risk interpretation.
• Learn the role of dashboards in public health surveillance and decision-making.
• Build conceptual understanding of AMR data visualization and reporting workflows.
• Interpret pathogen-wise, region-wise, and antibiotic-wise AMR trends.
• Understand One Health perspectives in AMR monitoring across human, animal, food, and environmental sources.

📅 Day 1: AMR Surveillance and Genomic Epidemiology Foundations

• Introduction to antimicrobial resistance as a global public health challenge
• AMR burden in clinical, environmental, food, and animal health settings
• Role of genomic epidemiology in AMR surveillance
• Overview of pathogen surveillance and infectious disease monitoring
• Genomic data types used in AMR studies
• AMR genes, resistance mechanisms, and antibiotic classes
• Public health metadata: sample source, location, date, pathogen, and resistance profile
• One Health approach for AMR surveillance
• Introduction to AI-driven AMR data interpretation and surveillance workflows

• Explore AMR surveillance datasets in Google Colab
• Understand sample metadata and resistance gene fields
• Perform basic data cleaning using Python
• Generate pathogen-wise and antibiotic class-wise summaries

📅 Day 2: AMR Gene Detection and Resistance Profiling

• Introduction to AMR gene detection workflows
• Overview of CARD/RGI and AMRFinderPlus-style outputs
• Understanding AMR gene annotation tables
• Resistance class and mechanism interpretation
• Pathogen-wise resistance profiling
• Detection of high-risk resistance genes and multidrug resistance patterns
• Preparing AMR data for AI-based analysis
• Pathogen-risk mapping for surveillance and reporting
• Research relevance of AMR profiling in genomic epidemiology

• Analyze AMR gene detection tables in Google Colab
• Interpret resistance gene, drug class, and mechanism columns
• Generate resistance profile summaries
• Create pathogen-wise AMR heatmaps and risk tables

📅 Day 3: AMR Dashboard Development for Public Health Intelligence

• AMR data cleaning and dashboard-ready data preparation
• Visualization of AMR trends by pathogen, gene, antibiotic class, source, and region
• Outbreak signal indicators and resistance alert concepts
• Nextstrain-style genomic epidemiology visualization overview
• Streamlit and Power BI dashboard planning
• Designing surveillance dashboards for researchers and public health teams
• Translating AMR data into actionable intelligence
• Dashboard storytelling for academic, clinical, and industry reporting
• Final AMR surveillance reporting workflow

• Create a basic AMR surveillance dashboard prototype
• Visualize AMR gene frequency, pathogen trends, and resistance distribution
• Prepare dashboard-ready charts and summary tables
• Generate a simple outbreak alert indicator using sample data

• Understand the role of genomic epidemiology in AMR surveillance.
• Interpret AMR gene profiles and antibiotic resistance patterns.
• Analyze pathogen-wise, source-wise, and region-wise AMR datasets.
• Understand how AI can support AMR pattern detection and surveillance intelligence.
• Prepare dashboard-ready AMR surveillance datasets.
• Design basic AMR surveillance dashboard components.
• Visualize resistance trends using charts and interactive dashboard concepts.
• Apply One Health principles to AMR surveillance across clinical, animal, food, and environmental settings.
• Generate public health insights from AMR genomic and metadata records.
• Communicate AMR surveillance findings in a structured and professional manner.