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+#' ID method
+#'
+#' This function implements a least squares estimation method of R0 due to
+#' Fisman et al. (PloS One, 2013). See details for implementation notes.
+#'
+#' 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.
+#'
+#' @param NT Vector of case counts.
+#' @param 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.
+#'
+#' @return \code{ID} returns a single value, the estimate of R0.
+#'
+#' @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.
+#' ID(NT, mu = 5 / 7)
+#'
+#' # Obtain R0 when the serial distribution has a mean of three days.
+#' ID(NT, mu = 3 / 7)
+#'
+#' @export
+ID <- function(NT, mu) {
+  NT <- as.numeric(NT)
+  TT <- length(NT)
+  s <- (1:TT) / mu
+  y <- log(NT) / s
+
+  R0_ID <- exp(sum(y) / TT)
+
+  return(R0_ID)
+}