Canary in the Coal Mine

Overview

The Canary in the Coal Mine Project (CCMP) is an AI-informed decision support system designed to detect early warning signals of emerging public health threats, grounded in One Health systems thinking. It integrates ecological, environmental, epidemiological, and social data to model how risks build and compound before reaching crisis levels. CCMP supports local, state, and regional health systems by translating complex, cross-sector indicators into actionable insights for anticipatory action and equitable resource allocation.

In Short: CCMP detects patterns across people, places, and ecosystems so health systems can act earlier, faster, and smarter.

Why It Matters

Most monitoring systems focus on single hazards or human-only data. CCMP identifies multi-signal patterns across ecosystems—combining indicators from climate, infrastructure, and animal/livestock health—to flag conditions approaching “tipping points.” These early warnings help prevent downstream harm, health impacts, service disruptions, and costly responses.

CCMP doesn’t replace NOAA forecasts or predict storms. Instead, it highlights vulnerabilities beneath them, translating multi-domain signals into proactive public health action.

How It Works

Raw Data

Subject Matter Experts (SMEs): Experts from diverse disciplines continuously contribute to and review CCMP’s growing knowledge base. Their expertise ensures data quality and interpretive accuracy.

Artificial Intelligence: Large language models and specialized search algorithms scan peer-reviewed research and reliable online sources across domains such as epidemiology, entomology, invasive species, environmental conditions (e.g., heat, humidity, soil moisture), infrastructure, and population vulnerability.

Custom Data Feeds: CCMP integrates real-time and historical data from federal, state, local, and private sources—covering everything from environmental metrics to hospital admissions. A custom ingestion pipeline ensures continuous updates and interoperability across datasets.

Curation: All incoming data is reviewed for accuracy, credibility, and community acceptance. Only verified, peer-reviewed, or widely trusted data is retained, minimizing misinformation and maintaining system integrity.

The Model

One Health Framework: CCMP applies the One Health approach to link interconnected events across human, animal, and environmental systems—revealing relationships not visible through siloed analysis.

AI-Driven Analysis: Large language models continuously evaluate stored data to identify emerging risks to public health and safety.

Compounding Risk Scoring: Each identified risk factor receives a numeric score. When multiple risk factors interact and their combined score exceeds defined thresholds, Canary Warnings are issued to alert stakeholders.

Why It’s Different

Many tools exist to forecast disasters or model emergencies. CCMP is distinct because it fuses AI processing power with curated, multidisciplinary data and human oversight. By applying a One Health perspective, CCMP can identify converging risks earlier and more accurately than conventional, single-domain systems.