nmode's Git Repositories - Rnaught/blob - R/ID.R

   1 #' ID method
   2 #'
   3 #' This function implements a least squares estimation method of R0 due to Fisman et al. (PloS One, 2013).
   4 #' See details for implementation notes.
   5 #'
   6 #' The method is based on a straightforward incidence decay model. The estimate of R0 is the value which
   7 #' minimizes the sum of squares between observed case counts and cases counts 'expected' under the model.
   8 #'
   9 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.
  10 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.
  11 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.
  12 #' Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.
  13 #'
  14 #' @param NT Vector of case counts.
  15 #' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts
  16 #'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one If case
  17 #'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.
  18 #'
  19 #' @return \code{ID} returns a single value, the estimate of R0.
  20 #'
  21 #' @examples
  22 #' ## ===================================================== ##
  23 #' ## Illustrate on weekly data                             ##
  24 #' ## ===================================================== ##
  25 #'
  26 #' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
  27 #' ## obtain Rhat when serial distribution has mean of five days
  28 #' ID(NT=NT, mu=5/7)
  29 #' ## obtain Rhat when serial distribution has mean of three days
  30 #' ID(NT=NT, mu=3/7)
  31 #'
  32 #' ## ========================================================= ##
  33 #' ## Compute Rhat using only the first five weeks of data      ##
  34 #' ## ========================================================= ##
  35 #'
  36 #' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
  37 #'
  38 #' @export
  39 ID <- function(NT, mu) {
  40     NT <- as.numeric(NT)
  41     TT <- length(NT)
  42     s <- (1:TT) / mu
  43     y <- log(NT) / s
  44
  45     R0_ID <- exp(sum(y) / TT)
  46
  47     return(R0_ID)
  48 }