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

The aim of this workshop is to equip participants with practical knowledge of AI-driven AMR surveillance, genomic epidemiology, and public health dashboard development for monitoring antimicrobial resistance patterns, resistance genes, pathogen spread, and risk indicators.

• 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 & Genomic Epidemiology Foundations

• Introduction to antimicrobial resistance as a global public health challenge.
• AMR burden across 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.

🛠️ Hands-on Lab (Google Colab)

• Task: Explore AMR surveillance datasets, clean sample metadata, and generate pathogen-wise and antibiotic class-wise summaries.
• Tools Covered: Python, Google Colab, pandas, AMR sample datasets, public health metadata templates.

🗓️ Day 2: AMR Gene Detection & 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.

🛠️ Hands-on Lab (Google Colab)

• Task: Analyze AMR gene detection tables, interpret resistance mechanisms, and generate pathogen-wise AMR profiles and risk visualizations.
• Tools Covered: Python, Google Colab, CARD/RGI-style outputs, AMRFinderPlus-style tables, pandas, matplotlib, seaborn.

🗓️ Day 3: AMR Dashboard Development & 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.

🛠️ Hands-on Lab (Google Colab)

• Task: Build a basic AMR surveillance dashboard, visualize resistance trends, and generate an outbreak alert indicator using sample data.
• Tools Covered: Python, Google Colab, Streamlit, Power BI, Nextstrain-style visualization concepts, matplotlib, Plotly.

• PhD Scholars & Researchers working in antimicrobial resistance, microbial genomics, genomic epidemiology, and pathogen surveillance.
• Microbiologists & Bioinformaticians working on AMR gene detection, resistance profiling, and microbial genomic data analysis.
• Public Health Researchers & Epidemiologists involved in AMR surveillance, outbreak monitoring, and infectious disease intelligence.
• Academicians & Faculty in microbiology, genomics, infectious diseases, bioinformatics, epidemiology, and public health.
• Industry & Clinical Professionals from diagnostics, biotechnology, genomics, healthcare analytics, pharmaceuticals, and clinical research working with AMR or pathogen surveillance 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.