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Outbreak Detection Threshold Calculator

Work out outbreak detection threshold instantly with clear inputs, formula shown and shareable results.

About Outbreak Detection Threshold Calculator

Sets an outbreak threshold at the historical mean plus a chosen number of standard deviations and compares the current count against it.

Formula

Mean plus 2SD threshold

Threshold = historical mean + k x historical standard deviation; z = (current cases - mean) / standard deviation

Worked example

With the default values below, Outbreak Detection Threshold Calculator returns Alert threshold of 18.000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Historical mean cases per periodaccepts 0.1–100,000
12
Historical standard deviationaccepts 0.01–10,000
3
Current period casesaccepts 0–1,000,000
20
Standard deviations above the meanaccepts 1–5
2

Results

Alert threshold
18.000 cases
Alert status
Above the threshold — investigate as a possible outbreak
Standard deviations above the mean
2.667
Cases above the threshold
2.00

Step by step

  1. 1Values used
  2. 2Mean plus 2SD threshold
  3. 3Alert threshold= 18.000 cases
  4. 4Alert status= Above the threshold — investigate as a possible outbreak
  5. 5Standard deviations above the mean= 2.667
  6. 6Cases above the threshold= 2.00

Assumptions and limits

  • The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax or fees or charges into account.
  • Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why mean plus two standard deviations?

Under a normal approximation it flags roughly the top 2.5% of periods, a workable false alarm rate for routine surveillance.

What if counts are low?

Poisson or negative binomial methods perform better than a normal approximation when counts are small.

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