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Bandicoot

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

RMAB Workflow

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.

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

System Architecture

  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

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)

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:

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

Architecture

System Components

System Architecture

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

Documentation

For Stakeholders

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

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

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).

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.

License

MIT License - See LICENSE for details.

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

Contact

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.

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