Outbreak Early-Warning: EWMA Surveillance Monitor

Public-health machine learning rarely means a black-box neural network — much of it is disciplined statistical monitoring applied at scale. This simulator recreates a syndromic-surveillance early-warning system: twelve regions report daily case counts, and an exponentially weighted moving average (EWMA) control chart watches each region's smoothed statistic against a control limit derived from its own baseline noise. Inject a simulated outbreak with an adjustable growth rate, tune the EWMA weight and alarm threshold, and watch how fast — or how falsely — the detector reacts as the 3D case-count towers rise and the affected region's color crosses from calm cyan to alarm red.