MedSupply AI

Inspiration

During disasters, medical demand can rise quickly while roads may be blocked and supplies may not reach the areas that need them most. We wanted to explore how data and AI could help emergency teams decide where medical supplies should be prioritized.

What it does

MedSupply AI is a proposed disaster medical-supply intelligence system. It analyzes factors such as medical demand, available stock, disaster severity, and road accessibility to identify areas that may face shortages in the next 24–72 hours.

The system would produce a priority list showing the affected area, required medical supply, shortage risk, and priority level.

How it would work

1. Estimate medical demand using population, previous/current demand, disaster severity, and relevant health trends.
2. Compare demand with available medical stock at nearby hospitals or warehouses.
3. Analyze road accessibility, blockages, distance, and alternative routes.
4. Combine these factors to calculate shortage risk and prioritize locations.
5. Provide an explainable result showing why an area received a high, medium, or low priority.

Example

Zone A — ORS — High Risk — Priority 1 Zone B — Antibiotics — Medium Risk — Priority 2 Zone C — Bandages — Low Risk — Priority 3

Why it matters

Instead of being only an inventory tracker, MedSupply AI is designed as a decision-support system that connects demand, supply, and accessibility to help emergency teams make faster and more informed allocation decisions.

Current stage

This submission presents the concept and planned approach. The next stage would be to build and test the system with sample disaster, supply, and road-accessibility data.

Built With

• data-science
• machine
• numpy
• pandas
• python
• scikit-learn