Surveillance and Data Analytics of Antimicrobial Resistance (AMR) in Public Health
Strengthening Public Health Intelligence Through AMR Surveillance and Data Science.
About This Course
Antimicrobial resistance threatens global health security and has become a critical focus of public health surveillance systems. Accurate monitoring of AMR patterns in hospitals, communities, and environmental sources is essential for preventing outbreaks, guiding antibiotic policies, and informing stewardship programs.
This workshop combines microbiology, epidemiology, and data science to provide participants with practical exposure to AMR surveillance workflows, including data collection, cleaning, visualization, and interpretation. Using real-world datasets from clinical and environmental sources, participants will learn how to identify trends, detect emerging resistance, and apply analytics for risk assessment and public health planning.
Aim
This workshop aims to equip participants with the essential knowledge and technical skills required to monitor, analyze, and interpret antimicrobial resistance (AMR) trends using public health surveillance data. It focuses on integrating epidemiological frameworks with modern data analytics, genomics-driven surveillance, and digital AMR monitoring platforms. Through hands-on sessions, participants will learn to leverage statistical tools, dashboards, and machine learning approaches to strengthen AMR surveillance and support evidence-based public health decision-making.
Workshop Objectives
- Understand the principles of AMR surveillance systems and public health reporting frameworks.
- Learn data analytics workflows for AMR datasets, including cleaning, visualization, and trend analysis.
- Apply epidemiological indicators (incidence, prevalence, resistance rates, MDR/XDR patterns).
- Use dashboards and computational tools for AMR monitoring (R, Python, Tableau, WHONET).
- Interpret AMR data to guide public health actions, stewardship decisions, and risk assessments.
Workshop Structure
Day 1 — AMR Surveillance Frameworks and Data Sources
- Lecture: Global AMR surveillance frameworks — GLASS, NARMS, and One Health initiatives.
- Demo: Accessing and understanding WHO GLASS & NCBI Pathogen Detection dashboards.
- Hands-on: Data retrieval and format harmonization (CSV/JSON/API).
- Activity: Introduction to metadata integration — pathogen, geography, and resistance class.
Day 2 — Data Analytics and Visualization
- Lecture: Fundamentals of AMR data analysis — trends, prevalence, and co-resistance.
- Hands-on:
- R track: dplyr, ggplot2 for trend and heatmap visualization.
- Python track: pandas, matplotlib, seaborn for AMR pattern analytics.
- Tableau track: Dashboard creation for hospital-level surveillance.
- Case Exercise: Visualizing ESBL-producing E. coli and Klebsiella trends in healthcare data.
Day 3 — Advanced Epidemiological Insights and Reporting
- Lecture: AMR data modeling — trend forecasting and spatial mapping.
- Hands-on: AMR hotspot identification using R (sf, leaflet) or Python (plotly, geopandas).
- Workshop: Building interactive dashboards for surveillance reporting.
- Discussion: Translating data insights into public health strategies and policy recommendations.
Who Should Enrol?
- Undergraduate/Postgraduate degree in Microbiology, Biotechnology, Public Health, Bioinformatics, Epidemiology, or related fields.
- Professionals in hospitals, diagnostic labs, public health agencies, or infectious disease surveillance units.
- Data scientists / ML engineers interested in applying analytics to epidemiology and microbiology.
- Individuals passionate about AMR, public health strategy, and data-driven decision-making.
Important Dates
Registration Ends
12/07/2025
IST 7:00 AM
Workshop Dates
12/07/2025 – 12/09/2025
IST 8:00 PM
Workshop Outcomes
- Ability to analyze AMR surveillance datasets using modern analytical tools.
- Skills to interpret epidemiological indicators and resistance rates for public health use.
- Competence in using WHONET, GLASS, and customizable dashboards.
- Ability to detect emerging AMR threats and support outbreak investigations.
- Understanding of how data informs antibiotic stewardship and health policy.
Fee Structure
Student Fee
₹1499 | $55
Ph.D. Scholar / Researcher Fee
₹2499 | $65
Academician / Faculty Fee
₹3499 | $80
Industry Professional Fee
₹4499 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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