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Flu Watch turns real WHO FluNet history into memory: CockroachDB holds multi-year season baselines, Bedrock explains anomalies in plain English, and a human approves every alert before it reaches the field.
The problem
Health teams act after cases spike. Manually scanning surveillance data delays response, and a missed signal costs lives while a false alarm burns scarce trust and capacity.
Flu Watch flips it: score first against real history, explain the anomaly in plain English, and put a named human in the loop before anything ships.
Data foundation · real, not synthetic
Every score traces back to actual WHO FluNet surveillance data — loaded into CockroachDB as a multi-year season baseline, not a demo dataset.
System architecture: WHO FluNet ingest → CockroachDB scoring → Bedrock explanation → human approval → audit log.
CockroachDB SQL Shell — 295 real rows in season_baseline.
CockroachDB tools · confirmed live
Hackathon rules require at least two CockroachDB tools. Here's the proof: the Managed MCP Server connection, and a distributed vector index rebuilt at 1024 dimensions to match Bedrock Titan V2 embeddings.
Left: Managed MCP Server connection. Right: SHOW INDEXES confirming the rebuilt VECTOR(1024) index on season_baseline.
Sentinel AI · AWS Bedrock
Why is this region flagged? Bedrock explains it in plain English, grounded in the actual scored numbers — no hallucinated advice.
Officer of the Day · the differentiator
Every flagged anomaly lands here, in front of a named reviewer, with the full explanation attached. Nothing reaches the field on the model's word alone.
Approve / Reject gate — Testville week 31 (WATCH) and week 32 (ALERT), both pending human review.
Audit trail · the decision, written back
Once approved, the decision writes straight back to CockroachDB — who reviewed it, when, and what they decided. A real audit trail, not UI theater.
Open source
MIT-licensed, public, with real commit history behind it.
From signal to accountable action
"No warning reaches the field without a human decision — logged, timestamped, accountable."