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AMR Surveillance Data Analysis and Reporting Course
Original price was: USD $120.00.USD $59.00Current price is: USD $59.00.
This course is a three-day applied program on AMR surveillance frameworks, data retrieval, analytics, visualization, hotspot mapping, and reporting. It teaches participants how to work with surveillance data from sources such as WHO GLASS, NCBI Pathogen Detection, and hospital-level datasets, then turn that data into interpretable outputs for research and public health use.Data retrieval, harmonization, trend analysis, co-resistance analysis, hotspot mapping, dashboard reporting
About the Course
This course is built around a problem serious learners already know well: AMR surveillance data is only as useful as the framework used to collect it, the metadata used to interpret it, and the analytical choices used to summarize it. Many training pages reduce antimicrobial resistance analytics to a software exercise. That misses the point.
AMR data analysis sits at the intersection of microbiology, epidemiology, informatics, and public health decision-making. It involves surveillance definitions, resistance classifications, organism metadata, geography, time series patterns, and reporting logic. If any one of those layers is weak, the output becomes harder to trust.
Why This Topic Matters
AMR surveillance has moved from a specialist concern to a core public health function. Health systems need clearer visibility into resistance trends, pathogen distribution, co-resistance profiles, and geographic concentration patterns.
The challenge is structural: surveillance data may come from national reporting systems, laboratory networks, hospitals, or genomics platforms, each with different formats and varying metadata quality. Comparing these sources without careful harmonization can create misleading conclusions.
Trends without denominator logic, metadata context, and resistance-class interpretation can hide more than they reveal.
What Participants Will Learn
• Explain GLASS and One Health frameworks
• Retrieve data from CSV, JSON, and APIs
• Harmonize inconsistent surveillance datasets
• Integrate metadata across pathogens and geography
• Analyze prevalence and temporal trends
• Create visuals in R, Python, or Tableau
• Examine co-resistance patterns reliably
• Identify hotspots through spatial mapping
• Build dashboards for institutional reporting
• Translate findings into policy-facing narratives
Course Structure / Table of Contents
Module 1 — AMR Surveillance Frameworks and Data Sources
- What AMR surveillance is designed to measure, and what it often misses
- Global surveillance architecture: WHO GLASS, NARMS, and One Health
- How surveillance definitions shape comparability across datasets
- Accessing and interpreting WHO GLASS and NCBI Pathogen Detection dashboards
- Understanding data provenance, scope, and reporting limitations
Module 2 — Data Retrieval and Harmonization
- Working with CSV, JSON, and API-based AMR data sources
- Cleaning and standardizing organism, geography, and resistance fields
- Metadata integration across pathogen, location, sample source, and resistance class
- Common harmonization issues in laboratory and surveillance datasets
- Building analysis-ready tables for downstream visualization and reporting
Module 3 — Fundamentals of AMR Data Analysis
- Measuring trends, prevalence, and resistance burden over time
- Reading co-resistance patterns without overinterpreting sparse data
- Structuring exploratory analysis for healthcare and public health settings
- Comparing organism-specific and resistance-class-specific patterns
- Interpreting denominators, missingness, and reporting bias
Module 4 — Visualization Workflows in R, Python, and Tableau
- R track: using dplyr and ggplot2 for trend plots and heatmaps
- Python track: using pandas, matplotlib, and seaborn for AMR pattern analytics
- Tableau track: designing dashboards for hospital-level surveillance
- Choosing chart types that fit epidemiological questions
- Building outputs for both technical and non-technical audiences
Module 5 — Case Exercise: ESBL Surveillance Patterns
- Visualizing ESBL-producing E. coli trends in healthcare data
- Comparing Klebsiella resistance patterns across settings or periods
- Interpreting variation across geography, facility type, or time
- Distinguishing noise from signals worth escalation
- Framing case findings for reporting and decision support
Module 6 — AMR Modeling, Spatial Mapping, and Hotspot Analysis
- Foundations of AMR trend forecasting and surveillance modeling
- Spatial logic in AMR data interpretation
- Hotspot identification using R (sf, leaflet) and Python (plotly, geopandas)
- Recognizing the limits of spatial interpretation in incomplete systems
Module 7 — Dashboards, Reporting, and Public Health Translation
- Building interactive dashboards for surveillance communication
- Designing reports for hospital leadership and surveillance teams
- Turning analytical outputs into policy-facing narratives
- Communicating uncertainty, caveats, and data limitations clearly
- Translating data insight into action without overstating certainty
Tools, Techniques, or Platforms Covered
WHO GLASS
R (dplyr, ggplot2, sf)
Python (pandas, seaborn)
Tableau
NCBI Pathogen Detection
Trend & Co-resistance Analysis
One Health Frameworks
R (dplyr, ggplot2, sf)
Python (pandas, seaborn)
Tableau
NCBI Pathogen Detection
Trend & Co-resistance Analysis
One Health Frameworks
Real-World Applications
Hospital Surveillance: Tracks organism-specific patterns, monitors ESBL trends, and supports stewardship teams through real-time dashboards.
Research: Facilitates reproducible analysis, cross-dataset comparison, and exploratory epidemiological modeling using global datasets.
Public Health Strategy: Turning analytical findings into policy-facing reports for One Health monitoring and national reporting programs.
Who Should Attend
- Epidemiologists, microbiologists, and public health analysts
- PhD scholars in AMR, pathogen data, or health analytics
- Postgraduate students in public health or health data science
- Hospital analysts supporting stewardship and infection prevention
- Technical professionals building public health dashboards
Prerequisites or Recommended Background
Basic familiarity with antimicrobial resistance concepts or infectious disease surveillance is beneficial. Some exposure to structured datasets and introductory experience with R, Python, or data visualization tools is helpful, though advanced programming is not required.
Why This Course Stands Out
This course starts with surveillance frameworks rather than just software syntax, providing critical context for why datasets differ. It offers multi-track workflows (R, Python, Tableau) to reflect real-world team operations and addresses the critical gap of translating data findings into policy narratives.
Frequently Asked Questions
What is this course about?
It is a three-day course on AMR surveillance data analysis, covering frameworks, retrieval, harmonization, visualization, hotspot mapping, and reporting.
Do I need prior coding experience?
Basic familiarity with R or Python is helpful, but advanced programming is not assumed. Tableau track participants can focus on reporting components.
Will the course include hands-on work?
Yes. The structure includes hands-on work in data retrieval, trend analysis, visualization, dashboard creation, and AMR hotspot mapping.
Is this course more relevant for research or public health practice?
Both. It supports research workflows in AMR analytics while also fitting hospital surveillance, stewardship reporting, and public health monitoring needs.
What kinds of AMR questions does the course help answer?
It helps participants analyze prevalence, temporal trends, co-resistance patterns, geographic concentration, and diagnostic reporting summaries.
Is this suitable for complete beginners?
Not really. It is more appropriate for learners with some prior exposure to AMR, epidemiology, or data analysis who want a structured and applied workflow.
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What You’ll Gain
- Full access to e-LMS
- Publication opportunity
- Self-assessment & final exam
- e-Certificate
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