Home▸Education & Learning Tools▸Sampling Bees for Viral Surveillance: A Protocol for Reliable Data

🧫 Sampling Bees for Viral Surveillance

An interactive 3D apiary where you choose a sample size and collection method and watch a live statistical confidence indicator show how reliably the sample represents true viral prevalence.

Education & Learning Tools3DModerate60 FPS

A 3D apiary of six hives holds a hidden population of bees with a true viral infection rate. Choose a sample size and collection method to draw bees from the apiary, and watch estimated prevalence and a live confidence meter respond.

🔬 What It Demonstrates

Margin of error shrinks with sample size, but the collection method matters just as much: a grab sample from a single hive inherits that hive's own bias and inflates the effective margin of error through a design effect, while spreading collection across hives is more statistically efficient.

🎮 How to Use

Set the true prevalence and sample size, then pick a collection method. Sampled bees turn red (infected) or green (clean) and swell in size; watch the estimated prevalence, margin of error, and reliability meter update instantly.

💡 Did You Know?

Because infection naturally clusters within a colony, a "big" sample of 100 bees from one hive can be statistically weaker than a stratified sample of 30 bees spread across several hives.

⚙ Under the hood

Interactive 3D apiary sampling scene where choosing sample size and collection method updates a simulated statistical confidence indicator showing how reliably the sample represents true viral prevalence.

viral-surveillancesampling-protocolbee-healthdisease-monitoringapicultureeducation

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install