AI-powered vaccination adherence for maternal and child health programs
Bandicoot is an open-source RMAB (Restless Multi-Armed Bandit) system that helps healthcare organizations intelligently prioritize which caregivers to contact, reducing childhood vaccination dropout rates by 20-30%.
200,000+ caregivers, limited resources, 30% dropout rate.
Traditional approaches waste resources:
- ❌ Universal SMS blasts contact everyone (80% don't need help)
- ❌ Random selection misses high-risk caregivers
- ❌ Manual triage doesn't scale beyond 1,000 caregivers
Result: Children miss critical vaccines, preventable diseases spread.
Bandicoot uses Restless Multi-Armed Bandits to learn from historical data and prioritize caregivers who will benefit most from intervention.
-
Learn Behavior Patterns
- Cluster 200K caregivers into ~20 behavioral groups
- Learn engagement dynamics (who responds to SMS? who needs calls?)
-
Compute Priority Scores
- Whittle index algorithm ranks caregivers by impact
- Higher score = higher marginal benefit from intervention
-
Optimize Daily Budget
- Given 1,000 contacts/day, recommend top 1,000 caregivers
- Maximize vaccination rate under resource constraints
-
Adapt & Improve
- Update based on SMS opens, clinic visits
- System learns and improves over time
Based on SAHELI deployment by Google Research & ARMMAN (serving 12M+ mothers in India):
| Metric | Before RMAB | With RMAB | Improvement |
|---|---|---|---|
| Vaccination Completion | 62% | 80% | +29% |
| SMS Engagement | 18% | 32% | +78% |
| Cost per Vaccination | $12.40 | $8.60 | -31% |
| Health Worker Efficiency | 15 calls/success | 10 calls/success | +50% |
Published: IAAI 2023 (Google AI for Social Good)
Want to deploy Bandicoot for your program?
See deployment guide for step-by-step setup.
Requirements:
- Historical SMS/call logs (6+ months)
- Vaccination records
- Cloud hosting (GCP, AWS, or Azure)
- Budget: ~$200/month for 200K caregivers
Interested in the theory and algorithms?
Read our theory documentation:
- RMAB Fundamentals - Mathematical foundations
- Healthcare Problem - Vaccination adherence challenge
- Our Solution - Bandicoot's architecture
Want to contribute or customize?
See technical design for architecture and implementation:
✅ Proven Approach - Based on SAHELI (Google/ARMMAN, 30% dropout reduction) ✅ Scalable - Handles 200K+ caregivers with <$200/month infrastructure ✅ Cloud-Agnostic - Works on GCP, AWS, Azure, or Kubernetes ✅ Privacy-First - No PII sharing, encrypted storage ✅ Open Source - MIT licensed, community-driven
Core Technologies:
- Python 3.10+ - Backend implementation
- FastAPI - REST API (OpenAPI docs auto-generated)
- PostgreSQL - Persistent storage (clusters, states, logs)
- Redis - Hot cache (Whittle indices for O(1) lookup)
- Serverless - Cloud Run (GCP), AWS Batch, or Azure Batch
Key Algorithms:
- Clustering - K-means on passive transition probabilities
- MDP Learning - Bayesian parameter estimation (bayesianbandits library)
- Whittle Index - Binary search + value iteration for priority scores
- Cold-Start - RandomForest classifier for new caregivers
- 📄 Project Purpose - Why we're building this
- 📊 MVP PRD - Product requirements and roadmap
- 📈 Expected Impact - Projected outcomes
- 🏗️ Technical Design - Architecture (7 modular docs)
- 🔬 Theory - RMAB fundamentals and healthcare application
- 📐 Diagrams - Visual architecture guides
- 💻 Implementation - Python source code (coming soon)
- 🎓 MedhAI Mentor Notes - Architectural critique by ex-Google Principal Engineer
- 📚 Chat Archive - Complete design discussion (5,909 lines)
- RMAB fundamentals research
- Technical design (7 modular docs)
- Architecture diagrams
- Cost optimization (<$200/month)
- Week 1-2: Core algorithms (clustering, Whittle solver)
- Week 3-4: API endpoints + Suvita integration
- Week 5-6: Deployment + monitoring
- Week 7-8: A/B test with 1,000 caregivers
- Expand to 50K → 200K caregivers
- Multi-channel optimization (SMS, calls, WhatsApp)
- Fairness constraints (geographic equity)
- Partner with additional NGOs
We welcome contributions! Areas where you can help:
- Code - Implement algorithms, improve performance
- Documentation - Tutorials, guides, translations
- Research - Test new RMAB variants, fairness metrics
- Deployment - Support new cloud providers, Kubernetes
- Testing - A/B test frameworks, simulation tools
See CONTRIBUTING.md for guidelines (coming soon).
- Google Research - SAHELI deployment (IAAI 2023)
- ARMMAN - Field studies with 12M+ mothers in India
- Suvita - 200K+ caregivers across Bihar, Uttar Pradesh
- MedhAI - Ex-Google Principal Engineer (architectural review)
- Verma, A. et al. (2023). "Restless Multi-Armed Bandits for Maternal and Child Health." IAAI.
- Mate, A. et al. (2022). "Field Study of Collapsing Bandits for Tuberculosis." AAAI.
- Whittle, P. (1988). "Restless Bandits: Activity Allocation in a Changing World." Journal of Applied Probability.
MIT License - See LICENSE for details.
Open-source to enable global health impact. Use freely, contribute back.
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: bandicoot@suvita.org (for deployment inquiries)
Built with ❤️ for maternal and child health
Bandicoot is named after the small marsupial that digs to find food - just like our system digs through data to find caregivers who need help.