Simple risk-based surveillance - calculation of sample size

Input Values

This page calculates the sample size for simple risk-based surveillance, for instance, a survey in which a high risk population is targeted.

This analysis assumes that there is no clustering of disease (for instance, we are working at the herd level), and that the effective specificity of the surveillance system is equal to one (all positives are followed up to ensure that they are not false positives).

One risk factor is considered, for which the following information is required:

• The relative risk: this measures the risk of animals being infected in the high-risk group, relative to the risk of animals being infected in the low-risk group. For risk-based surveillance, this should be greater than 1. If analysing biased surveillance (for instance abattoir testing), the animals tested may have lower probability of being infected than the rest of the population.
• The population proportion: this is the proportion of animals from the entire population that are in the high-risk group.
• The surveillance proportion: this is the proportion of animals from the surveillance that are in the high-risk group.

In addition, the following parameters are required:

• The design prevalence: this is the assumed prevalence of disease, if the disease is present in the population. It is used as a standard by which the sensitivity of the surveillance can be evaluated.
• The individual animal test sensitivity: this is the sensitivity of the test performed on individual animals.
• The target surveillance sensitivity: the probability that the surveillance system would detect at least one infected animal if disease was present at the specified design prevalence.

The results indicate the required sample size for the surveillance system,

For comparison, the sample size if representative sampling were used is also shown, along with the savings. This indicates how many fewer animals could be sampled using the risk-based approach acheives, relative to a representative approach.