diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/id.R | 48 | ||||
| -rw-r--r-- | R/idea.R | 62 | ||||
| -rw-r--r-- | R/server.R | 4 |
3 files changed, 57 insertions, 57 deletions
@@ -1,11 +1,11 @@ -#' ID method +#' Incidence Decay (ID) #' #' 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. +#' 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 @@ -14,33 +14,33 @@ #' 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. +#' @param cases Vector of case counts. The vector must be non-empty and only +#' contain positive integers. +#' @param 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 `mu` should be set to `1`. If case counts are daily and the serial +#' distribution has a mean of seven days, then `mu` should be set to `7`. #' -#' @return \code{ID} returns a single value, the estimate of R0. +#' @return An estimate of the basic reproduction number (R0). +#' +#' @references +#' [Fisman et al. (PloS One, 2013)]( +#' https://doi.org/10.1371/journal.pone.0083622) +#' +#' @seealso [idea()] for a similar method. +#' +#' @export #' #' @examples -#' # Weekly data: -#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) +#' # 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(NT, mu = 5 / 7) +#' id(cases, 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) +#' id(cases, mu = 3 / 7) +id <- function(cases, mu) { + exp(sum((log(cases) * mu) / seq_along(cases)) / length(cases)) } @@ -1,11 +1,11 @@ -#' IDEA method +#' Incidence Decay and Exponential Adjustment (IDEA) #' #' 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 +#' This method is closely related to that implemented in [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 case counts #' expected under the model. #' #' This method is based on an approximation of the SIR model, which is most @@ -15,42 +15,42 @@ #' 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. +#' @param cases Vector of case counts. The vector must be of length at least two +#' and only contain positive integers. +#' @param 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 `mu` should be set to `1`. If case counts are daily and the serial +#' distribution has a mean of seven days, then `mu` should be set to `7`. #' -#' @return \code{IDEA} returns a single value, the estimate of R0. +#' @return An estimate of the basic reproduction number (R0). +#' +#' @references +#' [Fisman et al. (PloS One, 2013)]( +#' https://doi.org/10.1371/journal.pone.0083622) +#' +#' @seealso [id()] for a similar method. +#' +#' @export #' #' @examples #' # Weekly data. -#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) +#' 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. -#' IDEA(NT, mu = 5 / 7) +#' idea(cases, 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 +#' idea(cases, mu = 3 / 7) +idea <- function(cases, mu) { + s <- seq_along(cases) / mu - y1 <- log(NT) / s - y2 <- s^2 - y3 <- log(NT) + x1 <- sum(s) + x2 <- sum(s^2) + x3 <- log(cases) - IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3) - IDEA2 <- TT * sum(y2) - sum(s)^2 - IDEA <- exp(IDEA1 / IDEA2) + y1 <- x2 * sum(x3 / s) - x1 * sum(x3) + y2 <- x2 * length(cases) - x1^2 - return(IDEA) - } + exp(y1 / y2) } @@ -303,10 +303,10 @@ eval_estimator <- function(input, output, estimator, dataset) { kappa = estimator$kappa)$Rhat, 2) # Incidence Decay else if (estimator$method == "ID") - estimate <- round(ID(unlist(dataset[3]), mu = serial), 2) + estimate <- round(id(unlist(dataset[3]), mu = serial), 2) # Incidence Decay with Exponential Adjustement else if (estimator$method == "IDEA") - estimate <- round(IDEA(unlist(dataset[3]), mu = serial), 2) + estimate <- round(idea(unlist(dataset[3]), mu = serial), 2) return(estimate) } |