Pandemic Pulse — Digital Biosurveillance

At a Glance

Developed a real-time bio-threat surveillance tool integrating social, search, and health data.
Won a national innovation challenge, gaining recognition for bio-threat detection.
Enabled outbreak alerts days ahead of official reports, showcasing AI’s public health impact.

Traditional outbreak surveillance lags weeks behind, missing early signals of new threats.
Critical signals spread across social media, web searches, and news are siloed and hard to monitor in real time.
Novel bio-threats start with sparse, noisy digital indicators that conventional systems often overlook.

Unified data pipeline continuously ingests multi-source feeds (Twitter, Google Trends, news) for anomaly detection.
Tiered filtering by pathogen priority and source credibility isolates the most credible weak signals.
Automated NLP and statistical algorithms flag unusual symptom clusters or location-based spikes in real time.

Modular cloud pipeline on AWS with engines for data extraction, enrichment, event detection, visualization, evaluation, and alert sharing.
Real-time ingestion from APIs (Twitter, Google) and RSS feeds funnels into a processing queue with feedback loops to refine signal quality.
Custom NLP enrichment and geo-clustering components clean data and add context (e.g., mapping tweets to locations) for accurate detection.
An interactive dashboard displays mapped alerts and timelines, updating continuously for early situational awareness.
A feedback mechanism updates keywords and data sources based on detected events, improving sensitivity over time.

Successfully flagged real events (e.g., Boston Marathon bombing, Hepatitis A outbreak) in retrospective tests, proving earlier detection than official reports.
Outperformed 69 other solutions to win DHS’s Hidden Signal Challenge, earning national recognition and stakeholder buy-in.
Delivered a functional prototype dashboard tested by public health officials, validating the tool’s practical use for early outbreak warning.

Technique/Skill	Tools/Implementation
Skill/Tool Category	Application in Pandemic Pulse — Digital Biosurveillance
Data Engineering & APIs	Python pipelines for real-time ingestion (Twitter API, Google Trends, RSS feeds)
Natural Language Processing	Regex filtering and keyword extraction to identify health threat signals
Cloud Infrastructure	AWS cloud deployment for scalable processing of high-volume streaming data
Algorithms & Analytics	Statistical anomaly detection, clustering algorithms, and custom social-media geolocation
Visualization & Dashboard	D3.js and map libraries to build an interactive outbreak monitoring dashboard
Collaboration & Management	Cross-sector coordination (federal agencies, health officials) and agile project management (Trello, GitHub) with strict data governance

Real-time integration of diverse, noisy data sources – a skill later applied to other domains (e.g., fusing IoT sensor feeds or hospital data streams).
Applying AI to public health: experience aligning technical solutions with epidemiological needs, a theme continued in pandemic monitoring and clinical ML projects.
Cross-sector collaboration: honed ability to unite government, healthcare, and tech partners, replicated in later projects like COVID-19 surveys and hospital safety models.

Challenge White Paper: “Pandemic Pulse: Using Digital Exhaust to Detect Bio-threats” – internal paper outlining the winning methodology.Challenge Announcement Winner Announcement
Prototype Dashboard: Interactive web dashboard (internal) demonstrated multi-source outbreak alerts in one view for agencies.
Related Research: Prior social-media surveillance studies (e.g., detecting foodborne illness from Twitter) provided the foundational methods used in this project.