Outbreak Early-Warning: EWMA Surveillance Monitor

Detector Parameters

EWMA weight λ 0.20

Alarm threshold k·σ 3.0

Outbreak growth rate r/day 0.00

Simulation speed 1 day/s

Surveillance Status

Day

Regions in alarm

Detection lag

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Outbreak region

How it works

Each of 12 simulated regions reports a daily case count x_t. Instead of alarming on raw noisy counts, the detector tracks an exponentially weighted moving average (EWMA) of each region's counts — the same statistic behind CDC-style early-aberration reporting systems (EARS):

z_t = λ·x_t + (1−λ)·z_(t−1),  0 < λ ≤ 1
UCL = μ₀ + k·σ₀·sqrt( λ / (2−λ) )

μ₀ and σ₀ are the mean and standard deviation estimated during a 14-day noise-only baseline window, before any outbreak is allowed to start. Because z_t averages recent history, it reacts to a sustained rise in cases while shrugging off single noisy days — smaller λ smooths harder but reacts slower; larger k reduces false alarms at the cost of a longer detection lag.

λ — how much weight the newest day's count gets in the running average.
k·σ — how many baseline standard deviations above the mean the smoothed statistic must climb before an alarm fires.
Outbreak growth rate — after the baseline window, the outbreak region's true mean grows as μ₀·(1+r)^{days since onset}; 0 keeps every region at baseline noise.
Each 3D column's height is that day's case count and its color is the EWMA z-score — it turns red the instant that region's z_t crosses its own control limit.

Real-world relevance: this is essentially the algorithm behind syndromic-surveillance early-warning systems used by health departments to catch outbreaks in emergency-room visit or pharmacy-sales data days before a formal case report confirms them.

Within control limit Elevated (> 60% of limit) Alarm (EWMA > UCL)

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