epiR (version 2.0.76)

rsu.sep.pass: Surveillance system sensitivity assuming passive surveillance and representative sampling within clusters

Description

Calculates the surveillance system (population-level) sensitivity for detection of disease for a passive surveillance system assuming comprehensive population coverage and sampling of clinical cases within diseased clusters.

Usage

rsu.sep.pass(N, n, step.p, pstar.c, p.inf.u, se.u)

Value

A list comprised of two elements:

se.p

scalar or vector, the estimated surveillance system (population-level) sensitivity of detection.

se.c

scalar or vector, the estimated cluster-level sensitivity of detection.

If step.p is a vector, scalars are returned. If step.p is a matrix, values are vectors of length equal to the number of rows in step.p.

Arguments

N

scalar or vector of length equal to the number of rows in step.p representing the population size.

n

scalar or vector of length equal to the number of rows in step.p representing the number of units tested per cluster.

step.p

vector or matrix of detection probabilities (0 to 1) for each step in the detection process. If a vector each value represents a step probability for a single calculation. If a matrix, columns are step probabilities and rows are simulation iterations.

pstar.c

scalar (0 to 1) or vector of length equal to the number of rows in step.p representing the cluster-level design prevalence.

p.inf.u

scalar (0 to 1) or vector of length equal to the number of rows in step.p representing the probability of disease in sampled and tested units. This is equivalent to the positive predictive value for a given prior probability of infection.

se.u

scalar (0 to 1) or vector of length equal to the number of rows in step.p, representing the unit sensitivity.

References

Lyngstad T, Hellberg H, Viljugrein H, Bang Jensen B, Brun E, Sergeant E, Tavornpanich S (2016). Routine clinical inspections in Norwegian marine salmonid sites: A key role in surveillance for freedom from pathogenic viral haemorrhagic septicaemia (VHS). Preventive Veterinary Medicine 124: 85 - 95. DOI: 10.1016/j.prevetmed.2015.12.008.

Examples

## EXAMPLE 1:
## A passive surveillance system for disease X operates in your country.
## There are four steps to the diagnostic cascade with detection probabilities 
## for each process of 0.10, 0.20, 0.90 and 0.99, respectively. Assuming the 
## probability that a unit actually has disease if it is submitted for 
## testing is 0.98, the sensitivity of the diagnostic test used at the unit 
## level is 0.90, the population is comprised of 1000 clusters (herds), 
## five animals from each cluster (herd) are tested and the cluster-level 
## design prevalence is 0.01, what is the sensitivity of disease detection 
## at the cluster (herd) and population level?

rsu.sep.pass(N = 1000, n = 5, step.p = c(0.10,0.20,0.90,0.99), 
   pstar.c = 0.01, p.inf.u = 0.98, se.u = 0.90)

## The sensitivity of disease detection at the cluster (herd) level is 0.018.
## The sensitivity of disease detection at the population level is 0.16.