Biotechnology · Workshop

Predictive Epidemiology: ML Frameworks for Global AMR Tracking and Surveillance

From Raw Data to Real-Time Insights: Building ML Frameworks for Epidemic Intelligence

ModeVirtual / Online
LevelModerate
Fee₹2,499

Batch Enroll More Workshop

Delivery Mode

Virtual / Online

Aim

The primary aim of this 3-day workshop is to bridge the gap between data science and public health by equipping participants with the theoretical knowledge and hands-on technical skills required to leverage Machine Learning (ML) and data analytics for global Antimicrobial Resistance (AMR) tracking, risk classification, and surveillance intelligence.

What Participants Will Learn

To achieve this aim, the workshop will focus on the following key operational objectives:

Demystify AMR Data Infrastructure: Introduce participants to global surveillance frameworks (like WHO GLASS) and specialized genomic databases (CARD, ResFinder, NCBI Pathogen Detection).
Build Data Proficiency: Provide practical training in Python-based data workflows to clean, manipulate, and visualize highly complex, multi-dimensional epidemiological and microbiological datasets.
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.
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

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

Public Health & Medical Professionals: Epidemiologists, public health officers, healthcare data analysts, and hospital infection control specialists looking to adopt data-driven predictive tools.
Life Sciences & Microbiology Researchers: Microbiologists, bioinformatics learners, and antibiotic stewardship teams who want to transition from traditional lab data analysis to advanced computational modeling.
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.

Predictive Epidemiology: ML Frameworks for Global AMR Tracking and Surveillance

About Workshop

Aim

What Participants Will Learn

Structure

📅 Day 1: Foundations of Predictive Epidemiology and Global AMR Surveillance

Hands-on Activity:

📅 Day 2: Machine Learning Models for AMR Prediction and Risk Classification

Hands-on Activity:

📅 Day 3: Global AMR Tracking, Genomic Surveillance and Early-Warning Dashboards

Hands-on Activity:

Important Dates

Workshop Dates

What You Will Gain

Outcomes

Who Should Attend

DR. H ANANDARAM

Department of Biotechnology