% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/IDEA.R
\name{IDEA}
\alias{IDEA}
\title{IDEA method}
\usage{
IDEA(NT, mu)
}
\arguments{
\item{NT}{Vector of case counts.}

\item{mu}{Mean of the serial distribution. This needs to match case counts in
time units. For example, if case counts are weekly and the serial
distribution has a mean of seven days, then \code{mu} should be set
to one. If case counts are daily and the serial distribution has a
mean of seven days, then \code{mu} should be set to seven.}
}
\value{
\code{IDEA} returns a single value, the estimate of R0.
}
\description{
This function implements a least squares estimation method of R0 due to
Fisman et al. (PloS One, 2013). See details for implementation notes.
}
\details{
This method is closely related to that implemented in \code{ID}. The method
is based on an incidence decay model. The estimate of R0 is the value which
minimizes the sum of squares between observed case counts and cases counts
expected under the model.

This method is based on an approximation of the SIR model, which is most
valid at the beginning of an epidemic. The method assumes that the mean of
the serial distribution (sometimes called the serial interval) is known. The
final estimate can be quite sensitive to this value, so sensitivity testing
is strongly recommended. Users should be careful about units of time (e.g.,
are counts observed daily or weekly?) when implementing.
}
\examples{
# Weekly data.
NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)

# Obtain R0 when the serial distribution has a mean of five days.
IDEA(NT, mu = 5 / 7)

# Obtain R0 when the serial distribution has a mean of three days.
IDEA(NT, mu = 3 / 7)

}