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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/id.R
\name{id}
\alias{id}
\title{Incidence Decay (ID)}
\usage{
id(cases, mu)
}
\arguments{
\item{cases}{Vector of case counts. The vector must be non-empty and only
contain positive integers.}
\item{mu}{Mean of the serial distribution. This must be a positive number.
The value should match the 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 \code{1}. If case counts are daily and the serial
distribution has a mean of seven days, then \code{mu} should be set to \code{7}.}
}
\value{
An estimate of the basic reproduction number (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{
The method is based on a straightforward 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.
cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
# Obtain R0 when the serial distribution has a mean of five days.
id(cases, mu = 5 / 7)
# Obtain R0 when the serial distribution has a mean of three days.
id(cases, mu = 3 / 7)
}
\references{
\href{https://doi.org/10.1371/journal.pone.0083622}{Fisman et al. (PloS One, 2013)}
}
\seealso{
\code{\link[=idea]{idea()}} for a similar method.
}
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