#' IDEA method #' #' This function implements a least squares estimation method of R0 due to #' Fisman et al. (PloS One, 2013). See details for implementation notes. #' #' 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. #' #' @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{IDEA} 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. #' IDEA(NT, mu = 5 / 7) #' #' # Obtain R0 when the serial distribution has a mean of three days. #' IDEA(NT, mu = 3 / 7) #' #' @export IDEA <- function(NT, mu) { if (length(NT) < 2) print("Warning: length of NT should be at least two.") else { NT <- as.numeric(NT) TT <- length(NT) s <- (1:TT) / mu y1 <- log(NT) / s y2 <- s^2 y3 <- log(NT) IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3) IDEA2 <- TT * sum(y2) - sum(s)^2 IDEA <- exp(IDEA1 / IDEA2) return(IDEA) } }