diff --git a/R/competing_hazards.R b/R/competing_hazards.R
index ed6890a5..6ef541e7 100644
--- 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
index eedd4029..7d76d873 100644
--- 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$clinical_treatment_drugs[
@@ -414,7 +323,7 @@ calculate_treated <- function(
       replace = TRUE
     )
   ])
-  
+
   #+++ DRUG EFFICACY +++#
   #+++++++++++++++++++++#
   effectively_treated_index <- bernoulli_multi_p(parameters$drug_efficacy[drugs])
@@ -422,16 +331,16 @@ calculate_treated <- function(
   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