replace instances of delayfn with onset_to_isolation

R/outbreak_model.R

@@ -97,10 +97,6 @@ outbreak_model <- function(num_initial_cases,
 
   # TODO: replace all instances of incfn with incubation_period
   incfn <- incubation_period
-
-  # TODO: replace all instances of delayfn with onset_to_isolation
-  delayfn <- onset_to_isolation
-
   # incfn <- dist_setup(dist_shape = 3.303525,dist_scale = 6.68849) # incubation function for ECDC run
 
   # Set initial values for loop indices
@@ -112,7 +108,7 @@ outbreak_model <- function(num_initial_cases,
   case_data <- outbreak_setup(num_initial_cases = num_initial_cases,
                               incfn = incfn,
                               prop_asym = prop_asym,
-                              delayfn = delayfn,
+                              onset_to_isolation = onset_to_isolation,
                               k = k)
 
   # Preallocate
@@ -131,7 +127,7 @@ outbreak_model <- function(num_initial_cases,
                          r0community = r0community,
                          r0subclin = r0subclin,
                          incfn = incfn,
-                         delayfn = delayfn,
+                         onset_to_isolation = onset_to_isolation,
                          prop_ascertain = prop_ascertain,
                          k = k,
                          quarantine = quarantine,

R/outbreak_setup.R

@@ -17,10 +17,22 @@
 #' # incubation period sampling function
 #' incfn <- dist_setup(dist_shape = 2.322737,dist_scale = 6.492272)
 #' # delay distribution sampling function
-#' delayfn <- dist_setup(delay_shape, delay_scale)
-#' outbreak_setup(num_initial_cases = 5,incfn,delayfn,k=1.95,prop_asym=0)
+#' onset_to_isolation <- function(x) {
+#'   rweibull(n = x, shape = 1.651524, scale = 4.287786)
+#' }
+#' outbreak_setup(
+#'   num_initial_cases = 5,
+#'   incfn,
+#'   onset_to_isolation,
+#'   k = 1.95,
+#'   prop_asym = 0
+#' )
 #'}
-outbreak_setup <- function(num_initial_cases, incfn, delayfn, k, prop_asym) {
+outbreak_setup <- function(num_initial_cases,
+                           incfn,
+                           onset_to_isolation,
+                           k,
+                           prop_asym) {
   # Set up table of initial cases
   inc_samples <- incfn(num_initial_cases)
 
@@ -36,7 +48,7 @@ outbreak_setup <- function(num_initial_cases, incfn, delayfn, k, prop_asym) {
 
   # 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

@@ -6,8 +6,6 @@
 #' @param case_data data.table of cases in outbreak so far; initially generated by outbreak_setup
 #' @param incfn `function` that samples from incubation period (Weibull
 #' distribution); generated by [dist_setup()]
-#' @param delayfn `function` that samples from the onset-to-hospitalisation
-#' delay (Weibull distribution); generated by [dist_setup()]
 #' @importFrom data.table data.table rbindlist
 #' @importFrom purrr map2 map2_dbl map_lgl
 #' @importFrom stats rbinom
@@ -20,23 +18,33 @@
 #' # incubation period sampling function
 #' incfn <- dist_setup(dist_shape = 2.322737,dist_scale = 6.492272)
 #' # delay distribution sampling function
-#' delayfn <- dist_setup(1.651524, 4.287786)
+#' onset_to_isolation <- function(x) {
+#'   rweibull(n = x, shape = 1.651524, scale = 4.287786)
+#' }
 #' # generate initial cases
-#' case_data <- outbreak_setup(num_initial_cases = 5,incfn,delayfn,k=1.95,prop_asym=0)
+#' case_data <- outbreak_setup(
+#'   num_initial_cases = 5,
+#'   incfn,
+#'   onset_to_isolation,
+#'   k = 1.95,
+#'   prop_asym = 0
+#' )
 #' # generate next generation of cases
-#' case_data <- outbreak_step(case_data = case_data,
-#' disp_iso = 1,
-#' disp_com = 0.16,
-#' r0isolated = 0,
-#' r0subclin = 1.25,
-#' disp_subclin = 0.16,
-#' r0community = 2.5,
-#' prop_asym = 0,
-#' incfn = incfn,
-#' delayfn = delayfn,
-#' prop_ascertain = 0,
-#' k = 1.95,
-#' quarantine = FALSE)[[1]]
+#' case_data <- outbreak_step(
+#'   case_data = case_data,
+#'   disp_iso = 1,
+#'   disp_com = 0.16,
+#'   r0isolated = 0,
+#'   r0subclin = 1.25,
+#'   disp_subclin = 0.16,
+#'   r0community = 2.5,
+#'   prop_asym = 0,
+#'   incfn = incfn,
+#'   onset_to_isolation = onset_to_isolation,
+#'   prop_ascertain = 0,
+#'   k = 1.95,
+#'   quarantine = FALSE
+#' )[[1]]
 #'}
 outbreak_step <- function(case_data,
                           prop_ascertain,
@@ -50,7 +58,7 @@ outbreak_step <- function(case_data,
                           prop_asym,
                           quarantine,
                           incfn,
-                          delayfn) {
+                          onset_to_isolation) {
 
   # For each case in case_data, draw new_cases from a negative binomial distribution
   # with an R0 and dispersion dependent on if isolated=TRUE
@@ -129,11 +137,11 @@ outbreak_step <- function(case_data,
   prob_samples[, isolated_time := ifelse(vect_is_true(asym), Inf,
                                          # If you are not asymptomatic, but you are missed,
                                          # you are isolated at your symptom onset
-                                         ifelse(vect_is_true(missed), onset + delayfn(1),
+                                         ifelse(vect_is_true(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_is_true(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)))]
 
 

tests/testthat/test-outbreak_setup.R

@@ -5,12 +5,12 @@ set.seed(20200410)
 test_that("A basic sim setup returns the correct object", {
   incfn <- dist_setup(dist_shape = 2.322737, dist_scale = 6.492272)
   # delay distribution sampling function
-  delayfn <- dist_setup(2, 4)
+  onset_to_isolation <- function(x) rweibull(n = x, shape = 2, scale = 4)
   # generate initial cases
   case_data <- outbreak_setup(
     num_initial_cases = 5,
     incfn = incfn,
-    delayfn = delayfn,
+    onset_to_isolation = onset_to_isolation,
     k = 1.95,
     prop_asym = 0
   )
@@ -23,13 +23,13 @@ test_that("A basic sim setup returns the correct object", {
 test_that("asym arg works properly", {
   incfn <- dist_setup(dist_shape = 2.322737, dist_scale = 6.492272)
   # delay distribution sampling function
-  delayfn <- dist_setup(2, 4)
+  onset_to_isolation <- function(x) rweibull(n = x, shape = 2, scale = 4)
   # generate initial cases
   # All asymptomatics
   all_asym <- outbreak_setup(
     num_initial_cases = 5,
     incfn = incfn,
-    delayfn = delayfn,
+    onset_to_isolation = onset_to_isolation,
     k = 1.95,
     prop_asym = 1
   )
@@ -41,7 +41,7 @@ test_that("asym arg works properly", {
   mix <- outbreak_setup(
     num_initial_cases = 10000,
     incfn = incfn,
-    delayfn  =  delayfn,
+    onset_to_isolation = onset_to_isolation,
     k = 1.95,
     prop_asym = 0.5
   )