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 is a rapidly escalating global health threat, impacting treatment outcomes, healthcare costs, and outbreak control. Public health systems rely on surveillance to detect emerging resistance patterns, monitor antibiotic usage, and guide interventions such as stewardship programs and infection control measures. However, AMR surveillance produces complex datasets spanning microbiology labs, hospitals, community testing, and national reporting systems—making data analytics essential for timely interpretation.
This workshop provides a practical framework for AMR surveillance and analytics, covering data sources (hospital labs, national AMR networks, WGS/NGS outputs), surveillance indicators, and methods for analyzing resistance trends and hotspots. Participants will learn to clean and structure AMR datasets, perform trend analysis, stratify resistance by region/pathogen/drug, detect anomalies, and communicate findings via visualizations and reports. The approach is dry-lab and analytics-focused, suitable for research, public health, and healthcare settings.
Aim
This workshop aims to train participants in AMR surveillance concepts and data analytics workflows used in public health decision-making. It covers how AMR data is collected, standardized, analyzed, and interpreted to track resistance trends across populations, hospitals, and communities. Participants will learn to work with real-world AMR datasets to generate actionable insights, dashboards, and risk indicators. The program bridges microbiology, epidemiology, and data science for evidence-based AMR control.
Workshop Objectives
- Understand AMR surveillance systems, indicators, and reporting structures.
- Learn AMR dataset structure: pathogens, antibiotics, MIC/AST results, metadata.
- Perform cleaning, standardization, and exploratory analysis of AMR data.
- Analyze trends, resistance rates, hotspots, and outbreak-like signals.
- Build dashboards and reports to support public health action and stewardship.
Workshop Structure
Day 1 — AMR Surveillance Frameworks and Data Sources
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?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
01/23/2026
IST 7:00 PM
Workshop Dates
01/23/2026 – 01/25/2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Interpret AMR surveillance data and key indicators used in public health.
- Build clean, analysis-ready AMR datasets from lab/hospital records.
- Generate antibiograms, trend plots, and resistance burden summaries.
- Identify hotspots, anomalies, and high-risk pathogen–drug pairs.
- Communicate findings through dashboards, reports, and decision-ready visuals.
Fee Structure
Student Fee
₹1799 | $70
Ph.D. Scholar / Researcher Fee
₹2799 | $80
Academician / Faculty Fee
₹3799 | $95
Industry Professional Fee
₹4799 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →