Bandicoot

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%.

Check https://github.com/bhi5hmaraj/bandicoot/tree/main for more info

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The Problem

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.

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Our Solution

Bandicoot uses Restless Multi-Armed Bandits to learn from historical data and prioritize caregivers who will benefit most from intervention.

How It Works

1. Learn Behavior Patterns

   • Cluster 200K caregivers into ~20 behavioral groups
   • Learn engagement dynamics (who responds to SMS? who needs calls?)

2. Compute Priority Scores

   • Whittle index algorithm ranks caregivers by impact
   • Higher score = higher marginal benefit from intervention

3. Optimize Daily Budget

   • Given 1,000 contacts/day, recommend top 1,000 caregivers
   • Maximize vaccination rate under resource constraints

4. Adapt & Improve

   • Update based on SMS opens, clinic visits
   • System learns and improves over time

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Proven Impact

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)

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Quick Start

For NGOs & Health Programs

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

For Researchers

Interested in the theory and algorithms?

Read our theory documentation:

1. RMAB Fundamentals - Mathematical foundations
2. Healthcare Problem - Vaccination adherence challenge
3. Our Solution - Bandicoot's architecture

For Developers

Want to contribute or customize?

See technical design for architecture and implementation:

• System Overview
• RMAB Algorithms
• API Design
• Deployment

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Features

✅ 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

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Architecture

System Components

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

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Documentation

For Stakeholders

• 📄 Project Purpose - Why we're building this
• 📊 MVP PRD - Product requirements and roadmap
• 📈 Expected Impact - Projected outcomes

For Engineers

• 🏗️ Technical Design - Architecture (7 modular docs)
• 🔬 Theory - RMAB fundamentals and healthcare application
• 📐 Diagrams - Visual architecture guides
• 💻 Implementation - Python source code (coming soon)

For Reviewers

• 🎓 MedhAI Mentor Notes - Architectural critique by ex-Google Principal Engineer
• 📚 Chat Archive - Complete design discussion (5,909 lines)

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Roadmap

✅ Phase 1: Design (Complete)

•  RMAB fundamentals research
•  Technical design (7 modular docs)
•  Architecture diagrams
•  Cost optimization (<$200/month)

⏳ Phase 2: MVP Implementation (6-8 weeks)

•  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

🔮 Phase 3: Scale & Iterate

•  Expand to 50K → 200K caregivers
•  Multi-channel optimization (SMS, calls, WhatsApp)
•  Fairness constraints (geographic equity)
•  Partner with additional NGOs

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Contributing

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).

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Partners & Credits

Inspiration

• Google Research - SAHELI deployment (IAAI 2023)
• ARMMAN - Field studies with 12M+ mothers in India

Current Deployment

• Suvita - 200K+ caregivers across Bihar, Uttar Pradesh

Mentorship

• MedhAI - Ex-Google Principal Engineer (architectural review)

References

1. Verma, A. et al. (2023). "Restless Multi-Armed Bandits for Maternal and Child Health." IAAI.
2. Mate, A. et al. (2022). "Field Study of Collapsing Bandits for Tuberculosis." AAAI.
3. Whittle, P. (1988). "Restless Bandits: Activity Allocation in a Changing World." Journal of Applied Probability.

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License

MIT License - See LICENSE for details.

Open-source to enable global health impact. Use freely, contribute back.

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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.