AIResAgTeam/SARS-CoV-2-Multi-Intent-KG

🦠 SARS-CoV-2 Multi-Intent Knowledge Graph Explorer

Interactive COVID-19 research assistant powered by Quantum LIMIT Graph

🎯 What Is This?

An interactive knowledge graph system for exploring COVID-19 research across multiple scientific domains. This space demonstrates the SARS-CoV-2 multi-intent module from the Quantum LIMIT Graph ecosystem.

✨ Features

🔍 Multi-Domain Research

Explore COVID-19 knowledge across 5 research domains:

• —🦠 Biology (Virology): Spike protein, viral mechanisms, ACE2 binding
• —🛡️ Immunology: Antibody response, T-cell immunity, immune escape
• —🧬 Variants (Genomics): Omicron, Delta, mutations (L452R, F486V, etc.)
• —💊 Treatments: Paxlovid, Remdesivir, monoclonal antibodies, vaccines
• —🏥 Public Health: Mask mandates, ventilation, social distancing policies

🎲 Interactive Features

1. 1.Query Decomposition - Break complex questions into domain-specific intents
2. 2.Graph Visualization - Interactive 3D visualization of knowledge relationships
3. 3.Hypothesis Explorer - Track multiple research hypotheses simultaneously
4. 4.Evidence Browser - View supporting evidence with DOI references
5. 5.Serendipity Tracker - Monitor exploration patterns and cross-domain jumps
6. 6.Rate-Distortion Analysis - Optimize evidence retrieval quality

🧬 Example Queries

• —"How does Omicron BA.5 affect vaccine efficacy?"
• —"What treatments work for different variants?"
• —"Do mask mandates reduce transmission?"
• —"Why does the spike protein bind to ACE2?"
• —"What mutations lead to immune escape?"

🏗️ Architecture

Knowledge Graph Structure

Node Types:
├── VirusNode (root SARS-CoV-2)
├── VirologyNode (spike protein, RBD, etc.)
├── ImmunologyNode (antibodies, T-cells)
├── VariantNode (Omicron, Delta, mutations)
├── TreatmentNode (Paxlovid, vaccines)
└── PublicHealthNode (policies, interventions)

Edge Types:
├── Causal (mutation → immune escape)
└── Correlative (treatment → outcome)

Integration with Quantum LIMIT

This module is part of the larger Quantum LIMIT Graph v2.4.0 ecosystem:

Quantum LIMIT Graph
├── EGG (Federated Orchestration)
├── SerenQA (Serendipity Tracking)
├── Level 5 AI Scientist
├── MuISQA (Multi-Intent QA)
└── SARS-CoV-2 Module ← This Space

🎪 Use Cases

1. Research Exploration

Navigate COVID-19 literature across multiple scientific domains with automatic intent detection.

2. Hypothesis Generation

Discover novel connections between variants, treatments, and outcomes through graph exploration.

3. Evidence Synthesis

Aggregate findings across studies with quality checks and provenance tracking.

4. Educational Tool

Learn about COVID-19 biology, immunology, and public health interventions interactively.

5. Multi-Domain Queries

Ask questions that span multiple research areas and get comprehensive answers.

📊 Built-In Dataset

The space includes real COVID-19 research data:

Variants

• —Omicron BA.5: L452R, F486V, R493Q mutations
• —Delta: L452R, T478K mutations
• —Original strain: Reference genome

Treatments

• —Paxlovid: 89% efficacy (DOI: 10.1056/NEJMoa2118542)
• —Remdesivir: Reduces hospitalization
• —Monoclonal antibodies: Variant-specific efficacy
• —mRNA vaccines: BNT162b2, mRNA-1273

Scientific Evidence

• —50+ peer-reviewed papers cited
• —PubMed, Nature, Cell, NEJM references
• —DOI links for verification

🔬 Advanced Features

Serendipity Traces

Track how the system explores multiple hypotheses:

• —Branching Factor: Average children per exploration step
• —Diversity Score: Shannon entropy of hypothesis distribution
• —Cross-Domain Jumps: Transitions between research domains
• —Exploration Depth: How far into the graph the system searches

Rate-Distortion Optimization

Balance retrieval quality and coverage:

• —Rate: Number of documents/evidence pieces retrieved
• —Distortion: Redundancy or noise in results
• —Knee Point: Optimal balance point
• —FGW Algorithm: Fused Gromov-Wasserstein optimization

Governance System

Quality control for research outputs:

• —Evidence threshold requirements per domain
• —Confidence score minimums
• —Cross-validation of findings
• —Provenance tracking

🚀 Technologies

• —Frontend: Gradio 5.49.1
• —Backend: Python 3.10+
• —Graph Library: NetworkX
• —Visualization: Plotly
• —Scientific Computing: NumPy, SciPy
• —Integration: Quantum LIMIT Graph ecosystem

📚 Scientific Background

Rate-Distortion Theory

Based on Shannon's information theory, used to optimize retrieval of scientific evidence while minimizing redundancy.

Multi-Intent Graphs

Nodes represent different research intents (transmissibility, vaccine efficacy) rather than just entities. Enables more sophisticated question answering.

Serendipity in Research

Tracks unexpected discoveries and cross-domain connections, inspired by historical scientific breakthroughs.

🎓 Academic References

Key papers that informed this work:

• —Omicron BA.5 mutations: doi:10.1038/s41586-022-04980-y
• —Transmissibility analysis: doi:10.1016/j.cell.2022.06.005
• —Paxlovid efficacy: doi:10.1056/NEJMoa2118542
• —Mask effectiveness: doi:10.1073/pnas.2015954118

🔗 Related Resources

• —Quantum LIMIT Graph GitHub
• —SARS-CoV-2 KG GitHub
• —SerenQA Framework
• —MuISQA Integration

🤝 Contributing

This is a research prototype. Feedback and contributions welcome!

📄 License

MIT License - See LICENSE file for details

🙏 Acknowledgments

• —COVID-19 research community for open data
• —Quantum LIMIT Graph development team
• —Hugging Face for hosting infrastructure

Version: 1.0.0 Status: ✅ Production Demo Last Updated: December 2025 Part of: Quantum LIMIT Graph v2.4.0

Built with ❤️ for COVID-19 research and scientific discovery