Antimalarial Resistance: Slow Parasite Clearance

DESCRIPTION

@@ -93,7 +93,7 @@ Suggests:
   ggplot2,
   covr,
   mgcv 
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Roxygen: list(markdown = TRUE)
 LinkingTo:
   Rcpp,

R/antimalarial_resistance.R

@@ -5,55 +5,54 @@
 #' @param parameters the model parameters
 #' @param drug the index of the drug which resistance is being set, as set by the set_drugs() function, in the parameter list
 #' @param timesteps vector of time steps for each update to resistance proportion and resistance outcome probability
-#' @param artemisinin_resistance vector of updates to the proportions of infections that are artemisinin resistant at time t
-#' @param partner_drug_resistance vector of updates to the proportions of infections that are partner-drug resistant at time t
-#' @param slow_parasite_clearance_prob vector of updates to the proportion of artemisinin-resistant infections that result in early treatment failure
-#' @param early_treatment_failure_prob vector of updates to the proportion of artemisinin-resistant infections that result in slow parasite clearance
-#' @param late_clinical_failure_prob vector of updates to the proportion of partner-drug-resistant infections that result in late clinical failure
-#' @param late_parasitological_prob vector of updates to the proportion of partner-drug-resistant infections that result in late parasitological failure
-#' @param reinfection_prob vector of updates to the proportion of partner-drug-resistant infections that result in reinfection during prophylaxis
+#' @param artemisinin_resistance_proportion vector of updates to the proportions of infections that are artemisinin resistant at time t
+#' @param partner_drug_resistance_proportion vector of updates to the proportions of infections that are partner-drug resistant at time t
+#' @param slow_parasite_clearance_probability vector of updates to the proportion of artemisinin-resistant infections that result in early treatment failure
+#' @param early_treatment_failure_probability vector of updates to the proportion of artemisinin-resistant infections that result in slow parasite clearance
+#' @param late_clinical_failure_probability vector of updates to the proportion of partner-drug-resistant infections that result in late clinical failure
+#' @param late_parasitological_failure_probability vector of updates to the proportion of partner-drug-resistant infections that result in late parasitological failure
+#' @param reinfection_during_prophylaxis_probability vector of updates to the proportion of partner-drug-resistant infections that result in reinfection during prophylaxis
 #' @param slow_parasite_clearance_time single value representing the mean time individual's experiencing slow parasite clearance reside in the treated state
 #' @export
 set_antimalarial_resistance <- function(parameters,
                                         drug,
                                         timesteps,
-                                        artemisinin_resistance,
-                                        partner_drug_resistance,
-                                        slow_parasite_clearance_prob,
-                                        early_treatment_failure_prob,
-                                        late_clinical_failure_prob,
-                                        late_parasitological_prob,
-                                        reinfection_prob,
+                                        artemisinin_resistance_proportion,
+                                        partner_drug_resistance_proportion,
+                                        slow_parasite_clearance_probability,
+                                        early_treatment_failure_probability,
+                                        late_clinical_failure_probability,
+                                        late_parasitological_failure_probability,
+                                        reinfection_during_prophylaxis_probability,
                                         slow_parasite_clearance_time) {
 
-  if(any(partner_drug_resistance > 0,
-         slow_parasite_clearance_prob > 0,
-         late_clinical_failure_prob > 0,
-         late_parasitological_prob > 0,
-         reinfection_prob > 0)) {
+  if(any(partner_drug_resistance_proportion > 0,
+         late_clinical_failure_probability > 0,
+         late_parasitological_failure_probability > 0,
+         reinfection_during_prophylaxis_probability > 0)) {
     stop("Parameters set for unimplemented feature - late clinical failure, late parasitological failure, or reinfection during prophylaxis")
   }
 
-  if(any(c(length(artemisinin_resistance),
-           length(partner_drug_resistance), 
-           length(slow_parasite_clearance_prob), 
-           length(early_treatment_failure_prob), 
-           length(late_clinical_failure_prob), 
-           length(late_parasitological_prob), 
-           length(reinfection_prob)) != length(timesteps))) {
+  if(any(c(length(artemisinin_resistance_proportion),
+           length(partner_drug_resistance_proportion), 
+           length(slow_parasite_clearance_probability), 
+           length(early_treatment_failure_probability), 
+           length(late_clinical_failure_probability), 
+           length(late_parasitological_failure_probability), 
+           length(reinfection_during_prophylaxis_probability)) != length(timesteps))) {
     stop("Length of one or more resistance parameter vectors does not match time steps specified for update")
   }
 
-  if(any(artemisinin_resistance < 0 | artemisinin_resistance > 1 |
-         partner_drug_resistance < 0 | partner_drug_resistance > 1)) {
+  if(any(artemisinin_resistance_proportion < 0 | artemisinin_resistance_proportion > 1 |
+         partner_drug_resistance_proportion < 0 | partner_drug_resistance_proportion > 1)) {
     stop("Artemisinin and partner-drug resistance proportions must fall between 0 and 1")
   }
 
-  if(any(slow_parasite_clearance_prob < 0 | slow_parasite_clearance_prob > 1 |
-         early_treatment_failure_prob < 0 | early_treatment_failure_prob > 1 |
-         late_clinical_failure_prob < 0 | late_clinical_failure_prob > 1 |
-         late_parasitological_prob < 0 | late_parasitological_prob > 1 |
-         reinfection_prob < 0 | reinfection_prob > 1)) {
+  if(any(slow_parasite_clearance_probability < 0 | slow_parasite_clearance_probability > 1 |
+         early_treatment_failure_probability < 0 | early_treatment_failure_probability > 1 |
+         late_clinical_failure_probability < 0 | late_clinical_failure_probability > 1 |
+         late_parasitological_failure_probability < 0 | late_parasitological_failure_probability > 1 |
+         reinfection_during_prophylaxis_probability < 0 | reinfection_during_prophylaxis_probability > 1)) {
     stop("Resistance outcome probabilities must fall between 0 and 1")
   }
 
@@ -81,13 +80,13 @@ set_antimalarial_resistance <- function(parameters,
 
   parameters$antimalarial_resistance_drug[[drug_index]] <- drug
   parameters$antimalarial_resistance_timesteps[[drug_index]] <- timesteps
-  parameters$prop_artemisinin_resistant[[drug_index]] <- artemisinin_resistance
-  parameters$prop_partner_drug_resistant[[drug_index]] <- partner_drug_resistance
-  parameters$slow_parasite_clearance_prob[[drug_index]] <- slow_parasite_clearance_prob
-  parameters$early_treatment_failure_prob[[drug_index]] <- early_treatment_failure_prob
-  parameters$late_clinical_failure_prob[[drug_index]] <- late_clinical_failure_prob
-  parameters$late_parasitological_failure_prob[[drug_index]] <- late_parasitological_prob
-  parameters$reinfection_during_prophylaxis[[drug_index]] <- reinfection_prob
+  parameters$artemisinin_resistance_proportion[[drug_index]] <- artemisinin_resistance_proportion
+  parameters$partner_drug_resistance_proportion[[drug_index]] <- partner_drug_resistance_proportion
+  parameters$slow_parasite_clearance_probability[[drug_index]] <- slow_parasite_clearance_probability
+  parameters$early_treatment_failure_probability[[drug_index]] <- early_treatment_failure_probability
+  parameters$late_clinical_failure_probability[[drug_index]] <- late_clinical_failure_probability
+  parameters$late_parasitological_failure_probability[[drug_index]] <- late_parasitological_failure_probability
+  parameters$reinfection_during_prophylaxis_probability[[drug_index]] <- reinfection_during_prophylaxis_probability
   parameters$dt_slow_parasite_clearance[[drug_index]] <- slow_parasite_clearance_time
 
   return(parameters)
@@ -122,13 +121,13 @@ get_antimalarial_resistance_parameters <- function(parameters, drugs, timestep)
     drug <- parameters$antimalarial_resistance_drug[[i]]
     treated_with_drug <- which(drugs == drug)
     resistance_timestep <- match_timestep(ts = parameters$antimalarial_resistance_timesteps[[i]], t = timestep)
-    artemisinin_resistance_proportion[treated_with_drug] <- parameters$prop_artemisinin_resistant[[i]][resistance_timestep]
-    partner_drug_resistance_proportion[treated_with_drug] <- parameters$prop_partner_drug_resistant[[i]][resistance_timestep]
-    slow_parasite_clearance_probability[treated_with_drug] <- parameters$slow_parasite_clearance_prob[[i]][resistance_timestep]
-    early_treatment_failure_probability[treated_with_drug] <- parameters$early_treatment_failure_prob[[i]][resistance_timestep]
-    late_clinical_failure_probability[treated_with_drug] <- parameters$late_clinical_failure_prob[[i]][resistance_timestep]
-    late_parasitological_failure_probability[treated_with_drug] <- parameters$late_parasitological_failure_prob[[i]][resistance_timestep]
-    reinfection_during_prophylaxis_probability[treated_with_drug] <- parameters$reinfection_during_prophylaxis[[i]][resistance_timestep]
+    artemisinin_resistance_proportion[treated_with_drug] <- parameters$artemisinin_resistance_proportion[[i]][resistance_timestep]
+    partner_drug_resistance_proportion[treated_with_drug] <- parameters$partner_drug_resistance_proportion[[i]][resistance_timestep]
+    slow_parasite_clearance_probability[treated_with_drug] <- parameters$slow_parasite_clearance_probability[[i]][resistance_timestep]
+    early_treatment_failure_probability[treated_with_drug] <- parameters$early_treatment_failure_probability[[i]][resistance_timestep]
+    late_clinical_failure_probability[treated_with_drug] <- parameters$late_clinical_failure_probability[[i]][resistance_timestep]
+    late_parasitological_failure_probability[treated_with_drug] <- parameters$late_parasitological_failure_probability[[i]][resistance_timestep]
+    reinfection_during_prophylaxis_probability[treated_with_drug] <- parameters$reinfection_during_prophylaxis_probability[[i]][resistance_timestep]
     dt_slow_parasite_clearance[treated_with_drug] <- parameters$dt_slow_parasite_clearance[[i]]
   }
 
@@ -144,4 +143,4 @@ get_antimalarial_resistance_parameters <- function(parameters, drugs, timestep)
 
   return(resistance_parameters)
 
-}
+}

R/disease_progression.R

@@ -19,15 +19,27 @@ update_infection <- function(
 }
 
 create_progression_process <- function(
-  state,
-  from_state,
-  to_state,
-  rate,
-  infectivity,
-  new_infectivity
-  ) {
+    state,
+    from_state,
+    to_state,
+    rate,
+    infectivity,
+    new_infectivity
+) {
   function(timestep) {
-    to_move <- state$get_index_of(from_state)$sample(1/rate)
+
+    # Retrieve the indices of all individuals in the to_move state:
+    index <- state$get_index_of(from_state)
+
+    # If the length of rate is greater than 1 (when it's a variable):
+    if (length(rate) > 1) {
+      rate <- rate$get_values(index)
+    }
+
+    # Sample the individuals to be moved into a new Bitset using the transition rate(s):
+    to_move <- index$sample(1/rate)
+
+    # Update the infection status of those individuals who are moving:
     update_infection(
       state,
       to_state,

R/human_infection.R

@@ -295,50 +295,81 @@ calculate_treated <- function(
     )
   ])
 
+  #+++ DRUG EFFICACY +++#
+  #+++++++++++++++++++++#
+  effectively_treated_index <- bernoulli_multi_p(parameters$drug_efficacy[drugs])
+  effectively_treated <- bitset_at(seek_treatment, effectively_treated_index)
+  drugs <- drugs[effectively_treated_index]
+  n_drug_efficacy_failures <- n_treat - effectively_treated$size()
+  renderer$render('n_drug_efficacy_failures', n_drug_efficacy_failures, timestep)
+
+  #+++ ANTIMALARIAL RESISTANCE +++#
+  #+++++++++++++++++++++++++++++++#
   if(parameters$antimalarial_resistance) {
     resistance_parameters <- get_antimalarial_resistance_parameters(
       parameters = parameters,
       drugs = drugs, 
       timestep = timestep
     )
-    unsuccessful_treatment_probability <- resistance_parameters$artemisinin_resistance_proportion * resistance_parameters$early_treatment_failure_probability
-    susceptible_to_treatment_index <- bernoulli_multi_p(p = 1 - unsuccessful_treatment_probability)
-    susceptible_to_treatment <- bitset_at(seek_treatment, susceptible_to_treatment_index)
-    drugs <- drugs[susceptible_to_treatment_index]
+
+    #+++ EARLY TREATMENT FAILURE +++#
+    #+++++++++++++++++++++++++++++++#
+    early_treatment_failure_probability <- resistance_parameters$artemisinin_resistance_proportion * resistance_parameters$early_treatment_failure_probability
+    successfully_treated_indices <- bernoulli_multi_p(p = 1 - early_treatment_failure_probability)
+    successfully_treated <- bitset_at(effectively_treated, successfully_treated_indices)
+    n_early_treatment_failure <- effectively_treated$size() - successfully_treated$size()
+    renderer$render('n_early_treatment_failure', n_early_treatment_failure, timestep)
+    drugs <- drugs[successfully_treated_indices]
+    dt_slow_parasite_clearance <- resistance_parameters$dt_slow_parasite_clearance[successfully_treated_indices]
+
+    #+++ SLOW PARASITE CLEARANCE +++#
+    #+++++++++++++++++++++++++++++++#
+    slow_parasite_clearance_probability <- resistance_parameters$artemisinin_resistance_proportion[successfully_treated_indices] *
+      resistance_parameters$slow_parasite_clearance_probability[successfully_treated_indices]
+    slow_parasite_clearance_indices <- bernoulli_multi_p(p = slow_parasite_clearance_probability)
+    slow_parasite_clearance_individuals <- bitset_at(successfully_treated, slow_parasite_clearance_indices)
+    renderer$render('n_slow_parasite_clearance', slow_parasite_clearance_individuals$size(), timestep)
+    non_slow_parasite_clearance_individuals <- successfully_treated$copy()$set_difference(slow_parasite_clearance_individuals)
+    renderer$render('n_successfully_treated', successfully_treated$size(), timestep)
+    dt_slow_parasite_clearance <- dt_slow_parasite_clearance[slow_parasite_clearance_indices]
+
   } else {
-    susceptible_to_treatment <- seek_treatment
+
+    successfully_treated <- effectively_treated
+    renderer$render('n_successfully_treated', successfully_treated$size(), timestep)
 
   }
 
-  n_early_treatment_failure <- n_treat - susceptible_to_treatment$size()
-  successfully_treated_index <- bernoulli_multi_p(parameters$drug_efficacy[drugs])
-  successfully_treated <- bitset_at(susceptible_to_treatment, successfully_treated_index)
-  successfully_treated_drugs <- drugs[successfully_treated_index]
-  n_treat_eff_fail <- susceptible_to_treatment$size() - length(successfully_treated_index)
-  renderer$render('n_early_treatment_failure', n_early_treatment_failure, timestep)
-  renderer$render('n_treat_eff_fail', n_treat_eff_fail, timestep)
-  renderer$render('n_treat_success', successfully_treated$size(), timestep)
-
-  # Update variables of those who have been successfully treated:
   if (successfully_treated$size() > 0) {
     variables$state$queue_update("Tr", successfully_treated)
     variables$infectivity$queue_update(
-      parameters$cd * parameters$drug_rel_c[successfully_treated_drugs],
+      parameters$cd * parameters$drug_rel_c[drugs],
       successfully_treated
     )
     variables$drug$queue_update(
-      successfully_treated_drugs,
+      drugs,
       successfully_treated
     )
     variables$drug_time$queue_update(
       timestep,
       successfully_treated
     )
+    if(parameters$antimalarial_resistance) {
+      variables$dt$queue_update(
+        parameters$dt,
+        non_slow_parasite_clearance_individuals
+      )
+      variables$dt$queue_update(
+        dt_slow_parasite_clearance,
+        slow_parasite_clearance_individuals
+      )
+    }
   }
+
   successfully_treated
+
 }
 
-
 #' @title Schedule infections
 #' @description
 #' Schedule infections in humans after the incubation period

R/model.R

@@ -74,9 +74,10 @@
 #' susceptible
 #'  * net_usage: the number people protected by a bed net
 #'  * mosquito_deaths: number of adult female mosquitoes who die this timestep
-#'  * n_early_treatment_failure: number of clinically treated individuals who experienced early treatment failure in this timestep
-#'  * n_treat_eff_fail: number of clinically treated individuals who's treatment failed due to drug efficacy
-#'  * n_treat_success: number of successfully treated individuals in this timestep
+#'  * n_drug_efficacy_failures: number of clinically treated individuals whose treatment failed due to drug efficacy
+#'  * n_early_treatment_failure: number of clinically treated individuals who experienced early treatment failure
+#'  * n_successfully_treated: number of clinically treated individuals who are treated successfully (includes individuals who experience slow parasite clearance)
+#'  * n_slow_parasite_clearance: number of clinically treated individuals who experienced slow parasite clearance
 #'
 #' @param timesteps the number of timesteps to run the simulation for (in days)
 #' @param parameters a named list of parameters to use

R/mortality_processes.R

@@ -27,7 +27,7 @@ create_mortality_process <- function(variables, events, renderer, parameters) {
       died <- individual::Bitset$new(pop)$insert(bernoulli_multi_p(deathrates))
       renderer$render('natural_deaths', died$size(), timestep)
     }
-    reset_target(variables, events, died, 'S', timestep)
+    reset_target(variables, events, died, 'S', parameters, timestep)
     sample_maternal_immunity(variables, died, timestep, parameters)
   }
 }
@@ -74,7 +74,7 @@ sample_maternal_immunity <- function(variables, target, timestep, parameters) {
   }
 }
 
-reset_target <- function(variables, events, target, state, timestep) {
+reset_target <- function(variables, events, target, state, parameters, timestep) {
   if (target$size() > 0) {
     # clear events
     to_clear <- c(
@@ -113,6 +113,11 @@ reset_target <- function(variables, events, target, state, timestep) {
 
     # onwards infectiousness
     variables$infectivity$queue_update(0, target)
+
+    # treated compartment residence time:
+    if(!is.null(variables$dt)) {
+      variables$dt$queue_update(parameters$dt, target)
+    }
 
     # zeta and zeta group and vector controls survive rebirth
   }