Early Warning Signal Detection System

Problem

Public health officials needed a way to detect emerging threats before they became crises. Traditional surveillance systems relied on confirmed cases and structured data, creating a detection lag that could be life-threatening. The challenge was identifying meaningful signals within noise across unstructured sources.

Approach

Collaborated with data scientists to design a signal detection framework that combines natural language processing, time-series analysis, and human-in-the-loop validation. Created an interface that visualizes signal strength, confidence levels, and contextual information to help analysts distinguish between false alarms and genuine threats. Built feedback loops that improve model accuracy over time.

Outcome

The system successfully identified three public health events 2-3 weeks earlier than traditional methods. False positive rate reduced by 60% through iterative design improvements. Now deployed across three regional health departments with plans for national expansion.

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