A newer version of the Gradio SDK is available: 6.13.0
title: SARS-CoV-2 Multi-Intent Knowledge Graph Explorer
emoji: 🦠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
🦠 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
- Query Decomposition - Break complex questions into domain-specific intents
- Graph Visualization - Interactive 3D visualization of knowledge relationships
- Hypothesis Explorer - Track multiple research hypotheses simultaneously
- Evidence Browser - View supporting evidence with DOI references
- Serendipity Tracker - Monitor exploration patterns and cross-domain jumps
- 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
🤝 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