\name{epi.edr}

\alias{epi.edr}

\title{
Estimated dissemination ratio
}

\description{
Computes estimated dissemination ratio on the basis of a vector of numbers (usually counts of incident cases identified on each day of an epidemic).
}

\usage{
epi.edr(dat, n = 4, conf.level = 0.95, nsim = 99, na.zero = TRUE)
}

\arguments{
  \item{dat}{a numeric vector listing the number of incident cases for each day of an epidemic.}
  \item{n}{scalar, defining the number of days to be used when computing the estimated dissemination ratio.}
  \item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.}
  \item{nsim}{scalar, defining the number of simulations to be used for the confidence interval calculations.}
  \item{na.zero}{logical, replace \code{NaN} or \code{Inf} values with zeros?}
}

\details{
In infectious disease epidemics the \emph{n}-day estimated dissemination ratio (EDR) at day \emph{i} equals the total number of incident cases between day \code{i} and day \code{[i - (n - 1)]} (inclusive) divided by the total number of incident cases between day \code{(i - n)} and day \code{(i - 2n) + 1} (inclusive). EDR values are often calculated for each day of an epidemic and presented as a time series analysis. If the EDR is consistently less than unity, the epidemic is said to be `under control.' 

A simulation approach is used to calculate confidence intervals around each daily EDR estimate. The numerator and denominator of the EDR estimate for each day is taken in turn and a random number drawn from a Poisson distribution, using the calculated numerator and denominator value as the mean. EDR is then calculated for these simulated values and the process repeated \code{nsim} times. Confidence intervals are then derived from the vector of simulated values for each day.
}

\value{
Returns the point estimate of the EDR and the lower and upper bounds of the confidence interval of the EDR.
}

\examples{
set.seed(123)
dat <- rpois(n = 50, lambda = 2) 
edr.04 <- epi.edr(dat, n = 4, conf.level = 0.95, nsim = 99, na.zero = TRUE)

## Plot:
plot(1:50, 1:50, xlim = c(0,25), ylim = c(0, 10), xlab = "Days",
   ylab = "Estimated dissemination ratio", type = "n", main = "")
lines(1:50, edr.04[,1], type = "l", lwd = 2, lty = 1, col = "blue")
lines(1:50, edr.04[,2], type = "l", lwd = 1, lty = 2, col = "blue")
lines(1:50, edr.04[,3], type = "l", lwd = 1, lty = 2, col = "blue")

}

\keyword{univar}