epiR: rsu.sep.pass – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

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)

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.

epiR

Tools for the Analysis of Epidemiological Data

v2.0.19

GPL (>= 2)

Authors

Mark Stevenson <mark.stevenson1@unimelb.edu.au> and Evan Sergeant <evansergeant@gmail.com> with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida, Geoff Jones, Sarah Pirikahu, Simon Firestone, Ryan Kyle, Johann Popp, Mathew Jay and Charles Reynard.

Initial release

2021-01-12