About Workshop
This 3-day online workshop introduces participants to the use of artificial intelligence, genomic epidemiology, and interactive dashboards for antimicrobial resistance (AMR) surveillance. The workshop focuses on understanding AMR datasets, resistance gene profiling, pathogen tracking, outbreak intelligence, and public health visualization. Participants will learn how genomic and epidemiological data can be transformed into meaningful dashboards for monitoring AMR trends across clinical, environmental, food, and One Health settings.
Aim
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.
What Participants Will Learn
- 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.
Structure
📅 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
Important Dates
Registration Ends
6:00 PM
Workshop Dates
2026-08-11
7:00 PM
7:00 PM
What You Will Gain
Outcomes
- 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.