sep.passive: Passive surveillance sensitivity

In RSurveillance: Design and Analysis of Disease Surveillance Activities

Description

Estimates the population sensitivity of a passive surveillance system. Assumes comprehensive population coverage and samling of representative affected units from infected clusters

Usage

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

Arguments

step.p	vector or matrix of detection probabilities 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.
p.inf.u	the probability of infection in units sampled, equivalent to the positive predictive value of clinical signs of disease (for a given prior probability of infection). Either a scalar or vector with length equal to number of rows in step.p.
se	unit sensitivity of test (proportion). Either a scalar or vector with length equal to number of rows in step.p.
N	population size. Either a scalar or vector with length equal to number of rows in step.p
n	number of units tested per cluster reporting suspected disease. Either a scalar or vector with length equal to number of rows in step.p
pstar.c	cluster-level design prevalence (proportion). Either a scalar or vector with length equal to number of rows in step.p

Value

a list of 2 elements, the estimated cluster-level and population-level sensitivities. If step.p is a vector, values are scalars, if step.p is a matrix, values are vectors with length equal to the number of rows in step.p

Examples

# examples for sep.passive
sep.passive(c(0.1, 0.2, 0.9, 0.99), 0.98, 0.9, 1000, 5, 0.01)
sep.passive(c(0.1, 0.5, 0.95, 0.99), 0.98, 0.9, 1000, 5, 0.01)
step.p<- matrix(runif(30), nrow=10)
p.inf.u<- runif(10, 0.98, 0.999)
se<- mc2d::rpert(10, 0.9, 0.95, 0.98)
sep.passive(step.p, p.inf.u, se, 10000, 10, 0.02)

RSurveillance documentation built on July 2, 2020, 2:33 a.m.