AMRnet User Tutorial¶

Welcome to AMRnet!¶

This tutorial will guide you through using AMRnet, a web platform that visualizes antimicrobial resistance (AMR) data from bacterial pathogens worldwide. AMRnet makes complex genomic surveillance data accessible to healthcare professionals, public health officials, researchers, and policymakers - regardless of their technical background in genomics or bioinformatics.

What is AMRnet?¶

AMRnet is a global surveillance dashboard that shows antimicrobial resistance patterns in disease-causing bacteria. Think of it as a “weather map” for antibiotic resistance - it helps you understand:

Where resistant bacteria are found geographically
When resistance patterns are changing over time
Which antibiotics are still effective in different regions
How bacterial strains are related to each other

The platform analyzes thousands of bacterial genome sequences from public databases and presents the findings in user-friendly visualizations.

Who Should Use AMRnet?¶

AMRnet is designed for a diverse audience:

Healthcare Professionals

Clinicians making treatment decisions
Infection control specialists
Clinical microbiologists

Public Health Officials

Epidemiologists tracking resistance patterns
Policy makers developing AMR strategies
Surveillance coordinators

Researchers & Academics

Students learning about AMR
Researchers studying resistance mechanisms
Healthcare educators

Global Health Organizations

WHO regional offices
National health ministries
International surveillance networks

Key Concepts Made Simple¶

Understanding Antimicrobial Resistance (AMR)¶

What is AMR? Antimicrobial resistance occurs when bacteria evolve to survive treatments that previously killed them. It’s like bacteria developing “armor” against antibiotics.

Why does it matter? When bacteria become resistant, common infections become harder to treat, leading to: - Longer hospital stays - Higher treatment costs - Increased risk of complications - Sometimes death from previously treatable infections

How do bacteria become resistant? - Genetic mutations: Random changes in bacterial DNA - Gene acquisition: Bacteria can share resistance genes with each other - Selection pressure: Overuse/misuse of antibiotics favors resistant strains

Understanding Bacterial Genomes¶

What is a genome? A genome is the complete set of genetic instructions for an organism - like a blueprint. For bacteria, this blueprint determines their characteristics, including resistance to antibiotics.

What is genome sequencing? Genome sequencing is like reading the bacterial blueprint to understand: - What species/strain the bacteria is - Which resistance genes it carries - How it relates to other bacterial samples

Why sequence bacterial genomes? - Identify resistance mechanisms - Track outbreak sources - Monitor resistance spread - Guide treatment decisions

Understanding Bacterial Classification¶

Bacteria are classified in a hierarchical system (like a family tree):

Species: The basic unit (e.g., Salmonella Typhi, Klebsiella pneumoniae)

Strains/Genotypes: Variants within a species with similar genetic makeup - Think of these as “subspecies” or “varieties” - Useful for tracking specific lineages - Some strains may be more resistant than others

Sequence Types (STs): Specific genetic “fingerprints” - Used to identify and track bacterial strains - Numbers like ST313, ST15 refer to specific genetic profiles

Getting Started with AMRnet¶

Accessing the Platform¶

Visit www.amrnet.org
No registration required - it’s completely free and open access
Works on any device with an internet connection (computer, tablet, phone)

Choosing Your Pathogen¶

Step 1: At the top of the page, you’ll see a menu to select different bacteria:

Salmonella Typhi: Causes typhoid fever
Klebsiella pneumoniae: Causes pneumonia, bloodstream infections
Neisseria gonorrhoeae: Causes gonorrhea
Escherichia coli: Causes diarrheal diseases, urinary tract infections
Shigella: Causes dysentery
Non-typhoidal Salmonella: Causes food poisoning, invasive disease

Step 2: Click on the pathogen you’re interested in to access its specific dashboard.

Understanding the Dashboard Layout¶

The Map View¶

What you see: A world map with countries colored by resistance levels

How to interpret colors: - Green/Blue: Lower resistance levels (antibiotics still work well) - Yellow/Orange: Moderate resistance levels - Red/Dark Red: High resistance levels (antibiotics less effective) - Grey: Insufficient data available

Key controls: - Right panel: Choose what resistance pattern to display - Left panel: Filter data by year, region, or bacterial strain

Data requirements: Countries need at least 20 bacterial samples for data to be displayed

The Variable Selection Panel (Right Side)¶

Resistance Variables: Choose which antibiotic resistance to display - Individual antibiotics (e.g., ciprofloxacin, ceftriaxone) - Resistance categories (MDR, XDR) - Specific resistance mechanisms

Genotype Variables: Display the distribution of bacterial strains - Useful for understanding which strains are circulating where

Understanding Data Visualizations¶

Resistance Frequency Graphs¶

What they show: The percentage of bacteria resistant to different antibiotics

How to read them: - X-axis: Different antibiotics or bacterial strains - Y-axis: Percentage resistant (0-100%) - Colors: Different categories (susceptible, resistant, intermediate)

Interactive features: - Click on bars to see detailed breakdowns - Hover for exact percentages and counts

Time Trend Graphs¶

What they show: How resistance patterns change over time

How to read them: - X-axis: Years - Y-axis: Percentage resistant - Lines: Different resistance patterns or strains

What to look for: - Increasing trends: Resistance is getting worse - Decreasing trends: Interventions may be working - Stable patterns: Resistance levels are consistent

Drug Resistance Profiles¶

What they show: Patterns of multiple drug resistance

Key patterns to understand: - Susceptible: Bacteria killed by the antibiotic - MDR (Multi-Drug Resistant): Resistant to 3+ antibiotics - XDR (Extensively Drug Resistant): Resistant to most available antibiotics

Clinical significance: - Higher resistance = fewer treatment options - XDR strains are particularly concerning for patient care

Pathogen-Specific Guidance¶

Salmonella Typhi (Typhoid Fever)¶

Clinical Context: - Causes typhoid fever, mainly in South Asia and Sub-Saharan Africa - Spread through contaminated food and water - Preventable by vaccination and improved sanitation

Key Resistance Patterns: - MDR: Resistant to ampicillin, chloramphenicol, trimethoprim-sulfamethoxazole - Ciprofloxacin resistance: Reduces effectiveness of first-line oral treatment - XDR: Resistant to MDR drugs plus ciprofloxacin and ceftriaxone

What to look for: - High MDR levels in endemic regions - Emerging XDR strains (especially concerning) - Travel-associated cases in non-endemic countries

Important Strains: - H58: Historically dominant MDR lineage - H58 XDR: Extremely concerning extensively resistant variant

Klebsiella pneumoniae¶

Clinical Context: - Causes pneumonia, bloodstream infections, urinary tract infections - Major hospital-acquired pathogen - Particularly dangerous for immunocompromised patients

Key Resistance Patterns: - ESBL: Extended-spectrum beta-lactamases (resist many antibiotics) - Carbapenemase: Resist carbapenems (last-resort antibiotics) - Colistin resistance: Resist the “antibiotic of last resort”

What to look for: - High ESBL rates globally - Emerging carbapenemase-producing strains - Hypervirulent strains that cause severe infections

Important Strains: - ST258: Major epidemic clone with carbapenem resistance - ST11: Common in Asia with extensive resistance

Neisseria gonorrhoeae (Gonorrhea)¶

Clinical Context: - Sexually transmitted infection - Can cause serious complications if untreated - WHO priority pathogen for AMR

Key Resistance Patterns: - Azithromycin resistance: Threatens dual therapy - Ceftriaxone resistance: Eliminates current first-line treatment - XDR: Resistant to multiple drug classes

What to look for: - Declining azithromycin susceptibility globally - Emerging ceftriaxone resistance - Regional differences in resistance patterns

Treatment Implications: - Dual therapy (ceftriaxone + azithromycin) is current standard - Resistance to either drug threatens treatment effectiveness

Practical Use Cases¶

For Clinicians¶

Treatment Decision Support: 1. Check local resistance rates for your pathogen of interest 2. Identify which antibiotics are still effective in your region 3. Monitor trends to anticipate future resistance issues

Example Workflow: 1. Patient presents with suspected typhoid fever 2. Check AMRnet for local Salmonella Typhi resistance rates 3. Avoid antibiotics with high local resistance 4. Choose empirical therapy based on local susceptibility patterns

For Public Health Officials¶

Surveillance and Monitoring: 1. Track resistance trends in your region over time 2. Compare your data to regional/global patterns 3. Identify emerging resistance threats

Policy Development: 1. Use data to guide antibiotic stewardship programs 2. Prioritize surveillance efforts for high-risk strains 3. Develop evidence-based treatment guidelines

Example Use Case: 1. Monitor XDR typhoid emergence in your region 2. Compare rates to neighboring countries 3. Implement targeted vaccination programs if needed

For Researchers¶

Hypothesis Generation: 1. Identify geographical hotspots of resistance 2. Investigate temporal trends in resistance emergence 3. Study relationships between strains and resistance patterns

Data for Analysis: 1. Download aggregate data for statistical analysis 2. Generate hypotheses for detailed genomic studies 3. Identify knowledge gaps for future research

Interpreting Results Correctly¶

Understanding Data Limitations¶

Sampling Bias: - Data reflects what has been sequenced, not necessarily what’s circulating - Some regions/countries have more data than others - Clinical samples may over-represent resistant strains

Temporal Lag: - Sequencing and analysis take time - Data may be 6-12 months behind current circulation - Recent trends may not yet be visible

Geographic Representation: - Urban areas often better represented than rural - Some countries have limited surveillance capacity - Travel-associated cases may be attributed to travel destination

Critical Interpretation Guidelines¶

Green Light Indicators (Data you can trust): - Countries with ≥20 samples in the time period - Data from unbiased surveillance studies - Consistent patterns across multiple years

Yellow Light Indicators (Interpret with caution): - Countries with 10-19 samples - Data from mixed sources - Rapidly changing patterns

Red Light Indicators (Be very careful): - Countries with <10 samples - Data primarily from outbreak investigations - Single time point measurements

Common Misinterpretations to Avoid¶

“No data = No resistance” - Grey areas on maps don’t mean no resistance - May indicate limited surveillance capacity

“Higher resistance = worse healthcare” - May reflect better surveillance systems - Could indicate more accurate diagnosis

“Recent changes = real trends” - Small sample fluctuations can create apparent trends - Look for consistent patterns over multiple years

Advanced Features¶

Downloading Data¶

CSV Downloads: - Access raw data behind visualizations - Use for your own analysis - Includes metadata about sample sources

PDF Reports: - Generate static reports for presentations - Include data descriptions and caveats - Useful for stakeholder communication

Comparing Time Periods¶

Before/After Analysis: 1. Set baseline time period (e.g., 2015-2017) 2. Compare to recent period (e.g., 2020-2022) 3. Look for significant changes in resistance patterns

Trend Analysis: 1. Use line graphs to visualize trends over time 2. Look for inflection points where trends change 3. Consider external factors (policy changes, outbreaks)

Multi-Pathogen Comparisons¶

Cross-Pathogen Insights: 1. Compare resistance patterns across different bacteria 2. Identify common resistance mechanisms 3. Assess overall AMR burden in a region

Best Practices for Different User Types¶

For Healthcare Students¶

Learning Objectives: - Understand global AMR patterns - Learn about resistance mechanisms - Practice interpreting epidemiological data

Recommended Exercises: 1. Compare resistance patterns between high and low-income countries 2. Track the emergence of XDR tuberculosis globally 3. Analyze the impact of vaccination on typhoid resistance

For Clinical Microbiologists¶

Laboratory Integration: - Compare local laboratory results to global patterns - Identify unusual resistance patterns for investigation - Guide selective reporting of susceptibility results

Quality Assurance: - Validate local resistance rates against regional data - Identify potential methodology issues - Benchmark laboratory performance

For Policymakers¶

Evidence-Based Decision Making: 1. Use data to prioritize AMR interventions 2. Allocate resources to high-risk areas/pathogens 3. Monitor impact of policy interventions

Stakeholder Communication: 1. Generate compelling visualizations for presentations 2. Demonstrate AMR burden to funding bodies 3. Build case for surveillance investments

Troubleshooting Common Issues¶

Technical Problems¶

Slow Loading: - Try refreshing the page - Check internet connection - Clear browser cache

Map Not Displaying: - Ensure JavaScript is enabled - Try different browser - Disable ad blockers temporarily

Data Not Updating: - Check filter settings - Ensure sufficient sample size - Verify time period selection

Interpretation Challenges¶

Unexpected Results: - Check sample sizes and sources - Review data curation warnings - Consider temporal factors

Missing Countries: - Insufficient data for display - Check minimum sample requirements - Consider regional aggregation

Conflicting Information: - Different data sources may vary - Check methodology descriptions - Consider temporal differences

Getting Help and Support¶

Documentation Resources: - Full technical documentation at docs.amrnet.org - Pathogen-specific guides in the user manual - Methodology papers linked in each dashboard

Community Support: - GitHub discussions for technical questions - Email support: amrnetdashboard@gmail.com - Twitter: @AMRnet_org for updates

Training Materials: - Video tutorials (coming soon) - Webinar recordings - Educational slide sets

Staying Updated¶

Platform Updates: - New pathogens added regularly - Enhanced visualizations - Improved data curation

Data Updates: - Monthly data refreshes for most pathogens - Quarterly major updates - Annual methodology reviews

Following AMRnet: - Subscribe to newsletters - Follow social media accounts - Join the GitHub community

Conclusion¶

AMRnet democratizes access to complex genomic surveillance data, making it actionable for diverse users worldwide. By understanding these fundamental concepts and following best practices, you can confidently use AMRnet to:

Make informed clinical decisions
Develop evidence-based policies
Conduct meaningful research
Educate others about AMR

Remember that AMRnet is a tool to support decision-making, not replace clinical judgment or local surveillance data. Always consider local context, data limitations, and consult relevant experts when making critical decisions.

Together, we can use data to combat antimicrobial resistance and preserve these life-saving medicines for future generations.