Resolved remaining merge conflict issues and renamed some probs to ra…

…tes in competing hazards resolution function (including conversion of rates to probs). Also ran the line ending git line, so testing to see if this has done the trick...?
mrc-ide · May 8, 2024 · 91d50f4 · 91d50f4
1 parent 903547a
commit 91d50f4
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diff --git a/R/competing_hazards.R b/R/competing_hazards.R
@@ -52,10 +52,10 @@ CompetingHazard <- R6::R6Class(
         'cbind',
         lapply(private$outcomes, function(x) x$rates)
       )
-      occur_prob <- rowSums(event_probs)
+      occur_rates <- rowSums(event_rates)
       occur_rng <- private$rng(private$size)
-      occurs <- occur_rng < occur_prob
-      norm_probs <- event_probs / occur_prob
+      occurs <- occur_rng < rate_to_prob(occur_rates)
+      norm_probs <- event_rates / occur_rates
       norm_probs[is.na(norm_probs)] <- 0
       cum_probs <- apply(norm_probs, 1, cumsum)
       event_rng <- private$rng(private$size)

diff --git a/R/human_infection.R b/R/human_infection.R
@@ -200,91 +200,6 @@ infection_process_resolved_hazard <- function(
   )
 }
 
-<<<<<<< HEAD
-#' @title Calculate overall infections for bitten humans
-#' @description
-#' Sample infected humans given prophylaxis and vaccination
-#' @param variables a list of all of the model variables
-#' @param bitten_humans bitset of bitten humans
-#' @param parameters model parameters
-#' @param renderer model render object
-#' @param timestep current timestep
-#' @noRd
-calculate_infections <- function(
-  variables,
-  bitten_humans,
-  parameters,
-  renderer,
-  timestep
-  ) {
-  source_humans <- variables$state$get_index_of(
-    c('S', 'A', 'U'))$and(bitten_humans)
-
-  b <- blood_immunity(variables$ib$get_values(source_humans), parameters)
-
-  source_vector <- source_humans$to_vector()
-
-  # calculate prophylaxis
-  prophylaxis <- rep(0, length(source_vector))
-  drug <- variables$drug$get_values(source_vector)
-  medicated <- (drug > 0)
-  if (any(medicated)) {
-    drug <- drug[medicated]
-    drug_time <- variables$drug_time$get_values(source_vector[medicated])
-    prophylaxis[medicated] <- weibull_survival(
-      timestep - drug_time,
-      parameters$drug_prophylaxis_shape[drug],
-      parameters$drug_prophylaxis_scale[drug]
-    )
-  }
-
-  # calculate vaccine efficacy
-  vaccine_efficacy <- rep(0, length(source_vector))
-  vaccine_times <- variables$last_eff_pev_timestep$get_values(source_vector)
-  pev_profile <- variables$pev_profile$get_values(source_vector)
-  # get vector of individuals who have received their 3rd dose
-  vaccinated <- vaccine_times > -1
-  pev_profile <- pev_profile[vaccinated]
-  if (length(vaccinated) > 0) {
-    antibodies <- calculate_pev_antibodies(
-      timestep - vaccine_times[vaccinated],
-      exp(sample_pev_param(pev_profile, parameters$pev_profiles, 'cs')),
-      invlogit(sample_pev_param(pev_profile, parameters$pev_profiles, 'rho')),
-      exp(sample_pev_param(pev_profile, parameters$pev_profiles, 'ds')),
-      exp(sample_pev_param(pev_profile, parameters$pev_profiles, 'dl')),
-      parameters
-    )
-    vmax <- vnapply(parameters$pev_profiles, function(p) p$vmax)
-    beta <- vnapply(parameters$pev_profiles, function(p) p$beta)
-    alpha <- vnapply(parameters$pev_profiles, function(p) p$alpha)
-    vaccine_efficacy[vaccinated] <- calculate_pev_efficacy(
-      antibodies,
-      vmax[pev_profile],
-      beta[pev_profile],
-      alpha[pev_profile]
-    )
-  }
-
-  prob <- b * (1 - prophylaxis) * (1 - vaccine_efficacy)
-  infected <- bitset_at(source_humans, bernoulli_multi_p(prob))
-
-  incidence_renderer(
-    variables$birth,
-    renderer,
-    infected,
-    source_humans,
-    prob,
-    'inc_',
-    parameters$incidence_rendering_min_ages,
-    parameters$incidence_rendering_max_ages,
-    timestep
-  )
-
-  infected
-}
-=======
->>>>>>> d694b08 (Infection and recovery competing hazard resolution.)
-
 #' @title Calculate clinical infections
 #' @description
 #' Sample clinical infections from all infections
@@ -382,28 +297,22 @@ calculate_treated <- function(
     timestep,
     renderer
 ) {
-<<<<<<< HEAD
-
+
   if(clinical_infections$size() == 0) {
     return(individual::Bitset$new(parameters$human_population))
   }
-
-=======
->>>>>>> d694b08 (Infection and recovery competing hazard resolution.)
+
   treatment_coverages <- get_treatment_coverages(parameters, timestep)
   ft <- sum(treatment_coverages)
-  
+
   if (ft == 0) {
     return(individual::Bitset$new(parameters$human_population))
   }
-  
+
   renderer$render('ft', ft, timestep)
   seek_treatment <- sample_bitset(clinical_infections, ft)
   n_treat <- seek_treatment$size()
-<<<<<<< HEAD
-=======
 
->>>>>>> d694b08 (Infection and recovery competing hazard resolution.)
   renderer$render('n_treated', n_treat, timestep)
 
   drugs <- as.numeric(parameters$clinical_treatment_drugs[
@@ -414,24 +323,24 @@ calculate_treated <- function(
       replace = TRUE
     )
   ])
-  
+
   #+++ DRUG EFFICACY +++#
   #+++++++++++++++++++++#
   effectively_treated_index <- bernoulli_multi_p(parameters$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$antimalarial_resistance) {
     resistance_parameters <- get_antimalarial_resistance_parameters(
       parameters = parameters,
-      drugs = drugs, 
+      drugs = drugs,
       timestep = timestep
     )
-    
+
     #+++ EARLY TREATMENT FAILURE +++#
     #+++++++++++++++++++++++++++++++#
     early_treatment_failure_probability <- resistance_parameters$artemisinin_resistance_proportion * resistance_parameters$early_treatment_failure_probability
@@ -441,7 +350,7 @@ calculate_treated <- function(
     renderer$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] *
@@ -452,14 +361,14 @@ calculate_treated <- function(
     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 {
-    
+
     successfully_treated <- effectively_treated
     renderer$render('n_successfully_treated', successfully_treated$size(), timestep)
-    
+
   }
-  
+
   if (successfully_treated$size() > 0) {
     variables$state$queue_update("Tr", successfully_treated)
     variables$infectivity$queue_update(
@@ -485,9 +394,9 @@ calculate_treated <- function(
       )
     }
   }
-  
+
   successfully_treated
-  
+
 }
 
 #' @title Schedule infections