rsu.sep.pass {epiR} R Documentation

## 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)
```

### 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.

### 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`.

### 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.

```

[Package epiR version 2.0.38 Index]