Generalise distributions input into functions

NAMESPACE

@@ -1,7 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 export(detect_extinct)
-export(dist_setup)
 export(extinct_prob)
 export(inf_fn)
 export(outbreak_model)
@@ -18,7 +17,6 @@ importFrom(future.apply,future_lapply)
 importFrom(purrr,map2)
 importFrom(purrr,map2_dbl)
 importFrom(purrr,map_lgl)
-importFrom(purrr,partial)
 importFrom(purrr,safely)
 importFrom(sn,rsn)
 importFrom(stats,as.formula)

R/aux_functions.R

@@ -1,25 +1,3 @@
-#' Create partial function to sample from gamma distributions
-#' @author Joel Hellewell
-#' @param dist_shape a positive `numeric` scalar: shape parameter of Weibull
-#'   distribution
-#' @param dist_scale a positive `numeric` scalar: scale parameter of Weibull
-#'   distribution
-#'
-#' @return partial function that takes a numeric argument for number of samples
-#' @export
-#' @importFrom purrr partial
-#'
-#' @examples
-#' incfn <- dist_setup(dist_shape = 2.32, dist_scale = 6.49)
-#' incfn(5)
-dist_setup <- function(dist_shape = NULL, dist_scale = NULL) {
-  out <- purrr::partial(rweibull,
-                 shape = dist_shape,
-                 scale = dist_scale)
-  return(out)
-}
-
-
 #' Samples the serial interval for given incubation period samples
 #'
 #' @param inc_samp a positive `numeric` vector: samples from the incubation
@@ -63,8 +41,8 @@ inf_fn <- function(inc_samp = NULL, k = NULL) {
 #'   r0community = 2.5,
 #'   disp.com = 0.16,
 #'   disp.iso = 1,
-#'   delay_shape = 1.65,
-#'   delay_scale = 4.28,
+#'   onset_to_isolation = \(x) stats::rweibull(n = x, shape = 1.65, scale = 4.28),
+#'   incubation_period = \(x) stats::rweibull(n = x, shape = 2.322737, scale = 6.492272),
 #'   k = 0,
 #'   quarantine = FALSE
 #' )
@@ -107,8 +85,8 @@ extinct_prob <- function(outbreak_df_week = NULL, cap_cases  = NULL, week_range
 #'   r0community = 2.5,
 #'   disp.com = 0.16,
 #'   disp.iso = 1,
-#'   delay_shape = 1.65,
-#'   delay_scale = 4.28,
+#'   onset_to_isolation = \(x) stats::rweibull(n = x, shape = 1.65, scale = 4.28),
+#'   incubation_period = \(x) stats::rweibull(n = x, shape = 2.322737, scale = 6.492272),
 #'   k = 0,
 #'   quarantine = FALSE
 #' )

R/outbreak_model.R

@@ -21,10 +21,14 @@
 #'   `TRUE` then traced contacts are isolated before symptom onset
 #' @param prop.asym a nonnegative `numeric` scalar: proportion of cases that
 #'   are completely asymptomatic (sublinical) (between 0 and 1)
-#' @param delay_shape a positive `numeric` scalar: shape parameter of delay
-#'   distribution
-#' @param delay_scale a positive `numeric` scalar: scale parameter of delay
-#'   distribution
+#' @param onset_to_isolation a `function`: a random number generating
+#'   `function` that accepts a single `integer` argument specifying the
+#'   length of the `function` output.
+#' @param incubation_period a `function`: a random number generating
+#'   `function` that samples from incubation period distribution, the
+#'   `function` accepts a single `integer` argument specifying the number of
+#'   times to sample the incubation period (i.e. length of the `function`
+#'   output).
 #' @param num.initial.cases a nonnegative `integer` scalar: number of initial
 #'   or starting cases which are all assumed to be missed.
 #' @param cap_cases a positive `integer` scalar: number of cumulative cases at
@@ -59,8 +63,8 @@
 #'   disp.com = 0.16,
 #'   disp.subclin = 0.16,
 #'   k = 0,
-#'   delay_shape = 1.65,
-#'   delay_scale = 4.28,
+#'   onset_to_isolation = \(x) stats::rweibull(n = x, shape = 1.65, scale = 4.28),
+#'   incubation_period = \(x) stats::rweibull(n = x, shape = 2.32, scale = 6.49),
 #'   prop.asym = 0,
 #'   quarantine = FALSE
 #' )
@@ -70,18 +74,8 @@ outbreak_model <- function(num.initial.cases = NULL, prop.ascertain = NULL,
                            r0isolated = NULL, r0community = NULL,
                            r0subclin = NULL, disp.iso = NULL,
                            disp.com = NULL, disp.subclin = NULL,
-                           k, delay_shape = NULL,
-                           delay_scale = NULL, prop.asym = NULL,
-                           quarantine = NULL) {
-
-  # Set up functions to sample from distributions
-  # incubation period sampling function
-  incfn <- dist_setup(dist_shape = 2.322737,
-                      dist_scale = 6.492272)
-  # incfn <- dist_setup(dist_shape = 3.303525,dist_scale = 6.68849) # incubation function for ECDC run
-  # onset to isolation delay sampling function
-  delayfn <- dist_setup(delay_shape,
-                        delay_scale)
+                           k, onset_to_isolation, incubation_period,
+                           prop.asym = NULL, quarantine = NULL) {
 
   # Set initial values for loop indices
   total.cases <- num.initial.cases
@@ -90,9 +84,9 @@ outbreak_model <- function(num.initial.cases = NULL, prop.ascertain = NULL,
 
   # Initial setup
   case_data <- outbreak_setup(num.initial.cases = num.initial.cases,
-                            incfn = incfn,
+                            incubation_period = incubation_period,
                             prop.asym = prop.asym,
-                            delayfn = delayfn,
+                            onset_to_isolation = onset_to_isolation,
                             k = k)
 
   # Preallocate
@@ -110,8 +104,8 @@ outbreak_model <- function(num.initial.cases = NULL, prop.ascertain = NULL,
                              r0isolated = r0isolated,
                              r0community = r0community,
                              r0subclin = r0subclin,
-                             incfn = incfn,
-                             delayfn = delayfn,
+                             incubation_period = incubation_period,
+                             onset_to_isolation = onset_to_isolation,
                              prop.ascertain = prop.ascertain,
                              k = k,
                              quarantine = quarantine,

R/outbreak_setup.R

@@ -21,20 +21,20 @@
 #'
 #' @examples
 #' # incubation period sampling function
-#' incfn <- dist_setup(dist_shape = 2.32, dist_scale = 6.49)
+#' incubation_period <- \(x) stats::rweibull(n = x, shape = 2.32, scale = 6.49)
 #' # delay distribution sampling function
-#' delayfn <- dist_setup(dist_shape = 1.65, dist_scale = 4.28)
+#' onset_to_isolation <- \(x) stats::rweibull(n = x, shape = 1.65, scale = 4.28)
 #' out <- outbreak_setup(
 #'   num.initial.cases = 1,
-#'   incfn = incfn,
-#'   delayfn = delayfn,
+#'   incubation_period = incubation_period,
+#'   onset_to_isolation = onset_to_isolation,
 #'   k = 1.95,
 #'   prop.asym = 0
 #' )
 #' out
-outbreak_setup <- function(num.initial.cases, incfn, delayfn, k, prop.asym) {
+outbreak_setup <- function(num.initial.cases, incubation_period, onset_to_isolation, k, prop.asym) {
   # Set up table of initial cases
-  inc_samples <- incfn(num.initial.cases)
+  inc_samples <- incubation_period(num.initial.cases)
 
   case_data <- data.table(exposure = rep(0, num.initial.cases), # Exposure time of 0 for all initial cases
                           asym = as.logical(rbinom(num.initial.cases, 1, prop.asym)),
@@ -45,7 +45,7 @@ outbreak_setup <- function(num.initial.cases, incfn, delayfn, k, prop.asym) {
                           new_cases = NA)
 
   # set isolation time for cluster to minimum time of onset of symptoms + draw from delay distribution
-  case_data <- case_data[, isolated_time := onset + delayfn(1)
+  case_data <- case_data[, isolated_time := onset + onset_to_isolation(1)
                          ][, isolated := FALSE]
 
   case_data$isolated_time[case_data$asym] <- Inf

R/outbreak_step.R

@@ -5,10 +5,8 @@
 #' @param case_data a `data.table`: cases in outbreak so far; initially
 #'   generated by [outbreak_setup()]
 #' @inheritParams outbreak_model
-#' @param incfn a `function` that samples from incubation period Weibull
-#'   distribution; generated using [dist_setup()]
-#' @param delayfn a `function` that samples from the onset-to-hospitalisation
-#'   delay Weibull distribution; generated using [dist_setup()]
+#' @param onset_to_isolation a `function` that samples from the
+#'   onset-to-hospitalisation delay Weibull distribution
 #'
 #' @importFrom data.table data.table rbindlist
 #' @importFrom purrr map2 map2_dbl map_lgl
@@ -24,14 +22,14 @@
 #'
 #' @examples
 #' # incubation period sampling function
-#' incfn <- dist_setup(dist_shape = 2.32, dist_scale = 6.49)
+#' incubation_period <- \(x) stats::rweibull(n = x, shape = 2.32, scale = 6.49)
 #' # delay distribution sampling function
-#' delayfn <- dist_setup(dist_shape = 1.65, dist_scale = 4.28)
+#' onset_to_isolation <- \(x) stats::rweibull(n = x, shape = 1.65, scale = 4.28)
 #' # generate initial cases
 #' case_data <- outbreak_setup(
 #'   num.initial.cases = 5,
-#'   incfn = incfn,
-#'   delayfn = delayfn,
+#'   incubation_period = incubation_period,
+#'   onset_to_isolation = onset_to_isolation,
 #'   k = 1.95,
 #'   prop.asym = 0
 #' )
@@ -46,8 +44,8 @@
 #'   r0subclin = 1.25,
 #'   r0community = 2.5,
 #'   prop.asym = 0,
-#'   incfn = incfn,
-#'   delayfn = delayfn,
+#'   incubation_period = incubation_period,
+#'   onset_to_isolation = onset_to_isolation,
 #'   prop.ascertain = 0,
 #'   k = 1.95,
 #'   quarantine = FALSE
@@ -56,9 +54,10 @@
 #' case_data
 outbreak_step <- function(case_data = NULL, disp.iso = NULL, disp.com = NULL,
                           r0isolated = NULL, r0community = NULL,
-                          prop.asym = NULL, incfn = NULL, delayfn = NULL,
-                          prop.ascertain = NULL, k = NULL, quarantine = NULL,
-                          r0subclin = NULL, disp.subclin = NULL) {
+                          prop.asym = NULL, incubation_period = NULL,
+                          onset_to_isolation = NULL, prop.ascertain = NULL,
+                          k = NULL, quarantine = NULL, r0subclin = NULL,
+                          disp.subclin = NULL) {
 
   # For each case in case_data, draw new_cases from a negative binomial distribution
   # with an R0 and dispersion dependent on if isolated=TRUE
@@ -91,7 +90,7 @@ outbreak_step <- function(case_data = NULL, disp.iso = NULL, disp.com = NULL,
   }
 
   # Compile a data.table for all new cases, new_cases is the amount of people that each infector has infected
-  inc_samples <- incfn(total_new_cases)
+  inc_samples <- incubation_period(total_new_cases)
 
   prob_samples <- data.table(
     # time when new cases were exposed, a draw from serial interval based on infector's onset
@@ -137,11 +136,11 @@ outbreak_step <- function(case_data = NULL, disp.iso = NULL, disp.com = NULL,
   prob_samples[, isolated_time := ifelse(vect_isTRUE(asym), Inf,
                                         # If you are not asymptomatic, but you are missed,
                                         # you are isolated at your symptom onset
-                                        ifelse(vect_isTRUE(missed), onset + delayfn(1),
+                                        ifelse(vect_isTRUE(missed), onset + onset_to_isolation(1),
                                                # If you are not asymptomatic and you are traced,
                                                # you are isolated at max(onset,infector isolation time) # max(onset,infector_iso_time)
                                                ifelse(!vect_isTRUE(rep(quarantine, total_new_cases)),
-                                                      pmin(onset + delayfn(1), pmax(onset, infector_iso_time)),
+                                                      pmin(onset + onset_to_isolation(1), pmax(onset, infector_iso_time)),
                                                       infector_iso_time)))]
 
 

R/parameter_sweep.R

@@ -25,8 +25,10 @@
 #'   expand.grid(
 #'     delay_group = list(data.table(
 #'       delay = c("SARS", "Wuhan"),
-#'       delay_shape = c(1.651524, 2.305172),
-#'       delay_scale = c(4.287786, 9.483875)
+#'       onset_to_isolation = c(
+#'         \(x) stats::rweibull(n = x, shape = 1.651524, scale = 4.287786),
+#'         \(x) stats::rweibull(n = x, shape = 2.305172, scale = 9.483875)
+#'       )
 #'     )),
 #'     k_group = list(data.table(
 #'       theta = c("<1%", "15%", "30%"),
@@ -96,8 +98,7 @@ parameter_sweep <- function(scenarios = NULL, samples = 1,
       r0subclin = ifelse(
         "subclin_R0" %in% names(scenarios), x$subclin_R0, x$index_R0),
       k = x$k,
-      delay_shape = x$delay_shape,
-      delay_scale = x$delay_scale,
+      onset_to_isolation = x$onset_to_isolation,
       prop.ascertain = x$control_effectiveness,
       quarantine = x$quarantine,
       prop.asym = x$prop.asym

R/scenario_sim.R

@@ -35,16 +35,17 @@
 #'   disp.iso = 1,
 #'   disp.com = 0.16,
 #'   k = 0.7,
-#'   delay_shape = 2.5,
-#'   delay_scale = 5,
+#'   onset_to_isolation = \(x) stats::rweibull(n = x, shape = 2.5, scale = 5),
+#'   incubation_period = \(x) stats::rweibull(n = x, shape = 2.32, scale = 6.49),
 #'   prop.asym = 0,
 #'   prop.ascertain = 0
 #' )
 #' res
 scenario_sim <- function(n.sim, prop.ascertain, cap_max_days, cap_cases,
                          r0isolated, r0community, disp.iso, disp.com, k,
-                         delay_shape, delay_scale, num.initial.cases, prop.asym,
-                         quarantine, r0subclin = NULL, disp.subclin = NULL) {
+                         onset_to_isolation, incubation_period,
+                         num.initial.cases, prop.asym, quarantine,
+                         r0subclin = NULL, disp.subclin = NULL) {
 
   # Set infectiousness of subclinical cases to be equal to clinical cases unless specified otherwise
   if(is.null(r0subclin)) {
@@ -65,8 +66,8 @@ scenario_sim <- function(n.sim, prop.ascertain, cap_max_days, cap_cases,
                                              disp.subclin = disp.subclin,
                                              disp.iso = disp.iso,
                                              disp.com = disp.com,
-                                             delay_shape = delay_shape,
-                                             delay_scale = delay_scale,
+                                             onset_to_isolation = onset_to_isolation,
+                                             incubation_period = incubation_period,
                                              k = k,
                                              prop.asym = prop.asym,
                                              quarantine = quarantine))

README.Rmd

@@ -55,8 +55,8 @@ res <- scenario_sim(
   r0community = 2.5, ## non-isolated individuals have R0 of 2.5
   disp.com = 0.16, ## dispersion parameter in the community
   disp.iso = 1, ## dispersion  parameter of those isolated
-  delay_shape = 1.651524, ## shape parameter of time from onset to isolation
-  delay_scale = 4.287786, ## scale parameter of time from onset to isolation
+  onset_to_isolation = \(x) stats::rweibull(n = x, shape = 1.651524, scale = 4.287786), ## time from onset to isolation
+  incubation_period = \(x) stats::rweibull(n = x, shape = 2.322737, scale = 6.492272), ## incubation period
   k = 0, ## skew of generation interval to be beyond onset of symptoms
   quarantine = FALSE ## whether quarantine is in effect
 )

_pkgdown.yml

@@ -7,7 +7,6 @@ reference:
   - title: Helper functions
   - subtitle: Probability distribution manipulation
     contents:
-    - dist_setup
     - inf_fn
   - subtitle: Loop wrappers for scenario modelling
     contents: