About Workshop
Aim
What Participants Will Learn
To achieve this aim, the workshop will focus on the following key operational objectives:
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Demystify AMR Data Infrastructure: Introduce participants to global surveillance frameworks (like WHO GLASS) and specialized genomic databases (CARD, ResFinder, NCBI Pathogen Detection).
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Build Data Proficiency: Provide practical training in Python-based data workflows to clean, manipulate, and visualize highly complex, multi-dimensional epidemiological and microbiological datasets.
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Deploy Predictive Frameworks: Guide participants through framing AMR challenges as ML problems, engineering relevant biological/geographical features, and building supervised models (such as Random Forests and XGBoost) to predict resistance risks.
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Translate Insights into Action: Teach participants how to design basic early-warning systems and interactive dashboards (using Streamlit and Plotly) that translate raw data into actionable public health intelligence.
Structure
📅 Day 1: Foundations of Predictive Epidemiology and Global AMR Surveillance
- Antimicrobial resistance: global burden, drivers, and public health impact
- Principles of infectious disease surveillance and predictive epidemiology
- AMR data sources: clinical, microbiological, genomic, hospital, environmental, and public health datasets
- Global AMR surveillance systems and reporting frameworks
- Data preparation for AMR analytics: missing values, categorical variables, resistance labels, and temporal trends
- Role of machine learning in AMR detection, trend analysis, and surveillance intelligence
- Tools covered: Google Colab, Python, Pandas, NumPy, Matplotlib, WHO GLASS resources, open AMR datasets
Hands-on Activity:
- AMR Surveillance Dataset Exploration in Google Colab: Clean sample AMR records, explore pathogen-antibiotic resistance patterns, visualize resistance trends, and prepare data for predictive modeling
📅 Day 2: Machine Learning Models for AMR Prediction and Risk Classification
- ML problem framing for AMR: classification, risk scoring, trend prediction, and hotspot identification
- Feature engineering using pathogen, antibiotic, geography, time, patient, and sample-level variables
- Supervised learning models: Logistic Regression, Random Forest, Gradient Boosting, and XGBoost concepts
- Model evaluation for AMR prediction: accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix
- Interpreting model outputs for epidemiological and public health decision-making
- Bias, uncertainty, data quality, and responsible AI considerations in healthcare surveillance
- Tools covered: Google Colab, Python, Scikit-learn, Statsmodels, Plotly, Seaborn
Hands-on Activity:
- Building an AMR Risk Classification Model: Develop a machine learning model to classify resistance risk, evaluate performance, identify important predictors, and interpret results for surveillance use cases
📅 Day 3: Global AMR Tracking, Genomic Surveillance and Early-Warning Dashboards
- Global AMR tracking: geographic trends, temporal patterns, and hotspot analysis
- Integrating clinical, epidemiological, genomic, and environmental AMR signals
- Introduction to genomic surveillance for AMR: resistance genes, pathogen lineages, and mutation tracking
- AMR databases and platforms: CARD, ResFinder, NCBI Pathogen Detection, Microreact, and Nextstrain
- Early-warning frameworks for emerging resistance threats
- Designing interactive dashboards for AMR surveillance and public health reporting
- Tools covered: Streamlit, Plotly, CARD, ResFinder, NCBI Pathogen Detection, Microreact, Nextstrain, GitHub-based open datasets
Hands-on Activity:
- AMR Surveillance Dashboard and Early-Warning Risk Tracker: Create a simple dashboard concept to visualize resistance trends, identify high-risk pathogen-antibiotic combinations, highlight geographic or temporal patterns, and generate basic early-warning insights
Important Dates
Workshop Dates
What You Will Gain
Outcomes
- Understand the global significance of antimicrobial resistance and predictive epidemiology
- Identify key AMR data sources used in public health, microbiology, and genomic surveillance
- Clean, analyze, and visualize AMR surveillance datasets using Python workflows
- Apply machine learning models for AMR prediction and risk classification
- Evaluate model performance using healthcare-relevant metrics
- Interpret AMR trends across pathogens, antibiotics, regions, and time periods
- Understand the role of genomic surveillance in resistance tracking
- Explore open AMR platforms and databases for research and surveillance
- Design simple early-warning and dashboard frameworks for AMR monitoring
- Connect ML-based AMR analytics with public health decision-making and global surveillance priorities
Who Should Attend
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Public Health & Medical Professionals: Epidemiologists, public health officers, healthcare data analysts, and hospital infection control specialists looking to adopt data-driven predictive tools.
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Life Sciences & Microbiology Researchers: Microbiologists, bioinformatics learners, and antibiotic stewardship teams who want to transition from traditional lab data analysis to advanced computational modeling.
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Data Scientists & Computational Chemists: Computational biologists and industry professionals in pharmaceuticals or global health policy who want to apply ML architectures to urgent, real-world biosecurity and infectious disease threats.