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recovery_rates variable has been renamed as (disease) progression_rates. #334

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merged 2 commits into from
Sep 11, 2024
Merged

recovery_rates variable has been renamed as (disease) progression_rates. #334

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4bebd42
recovery_rates variable has been renamed as (disease) progression_rates.
RJSheppard Sep 10, 2024
2556bd6
Minor documentation and performance fixed.
RJSheppard Sep 10, 2024
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36 changes: 19 additions & 17 deletions R/disease_progression.R
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Original file line number Diff line number Diff line change
@@ -1,34 +1,34 @@
#' @title Calculate recovery rates
#' @description Calculates recovery rates for each individual in the population
#' @title Calculate disease progression rates
#' @description Calculates disease progression rates for each individual in the population
#' for storage in competing hazards object and subsequent resolution
#'
#' @param variables the available human variables
#' @param recovery_outcome competing hazards object for recovery rates
#' @param progression_outcome competing hazards object for disease progression rates
#' @noRd
create_recovery_rates_process <- function(
create_progression_rates_process <- function(
variables,
recovery_outcome
progression_outcome
) {
function(timestep){
target <- variables$state$get_index_of("S")$not()
recovery_outcome$set_rates(
progression_outcome$set_rates(
target,
variables$recovery_rates$get_values(target))
variables$progression_rates$get_values(target))
}
}


#' @title Disease progression outcomes (recovery)
#' @title Disease progression outcomes
#' @description Following resolution of competing hazards, update state and
#' infectivity of sampled individuals
#'
#' @param timestep the current timestep
#' @param target the sampled recovering individuals
#' @param target the sampled progressing individuals
#' @param variables the available human variables
#' @param parameters model parameters
#' @param renderer competing hazards object for recovery rates
#' @param renderer competing hazards object for disease progression rates
#' @noRd
recovery_outcome_process <- function(
progression_outcome_process <- function(
timestep,
target,
variables,
Expand All @@ -48,7 +48,7 @@ recovery_outcome_process <- function(
"U",
variables$infectivity,
parameters$cu,
variables$recovery_rates,
variables$progression_rates,
1/parameters$du,
variables$state$get_index_of("A")$and(target)
)
Expand All @@ -58,7 +58,7 @@ recovery_outcome_process <- function(
"S",
variables$infectivity,
0,
variables$recovery_rates,
variables$progression_rates,
0,
variables$state$get_index_of(c("U","Tr"))$and(target)
)
Expand All @@ -73,19 +73,21 @@ recovery_outcome_process <- function(
#' @param to_state the destination disease state
#' @param infectivity the handle for the infectivity variable
#' @param new_infectivity the new infectivity of the progressed individuals
#' @param progression_rates the handle for the progression_rates variable
#' @param new_progression the new disease progression rate of the progressed individuals
#' @noRd
update_infection <- function(
state,
to_state,
infectivity,
new_infectivity,
recovery_rates,
new_recovery_rate,
progression_rates,
new_progression_rate,
to_move
) {
state$queue_update(to_state, to_move)
infectivity$queue_update(new_infectivity, to_move)
recovery_rates$queue_update(new_recovery_rate, to_move)
progression_rates$queue_update(new_progression_rate, to_move)
}

#' @title Modelling the progression to asymptomatic disease
Expand Down Expand Up @@ -115,7 +117,7 @@ update_to_asymptomatic_infection <- function(
new_infectivity,
to_move
)
variables$recovery_rates$queue_update(
variables$progression_rates$queue_update(
1/parameters$da,
to_move
)
Expand Down
124 changes: 82 additions & 42 deletions R/human_infection.R
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Original file line number Diff line number Diff line change
Expand Up @@ -347,13 +347,73 @@ calculate_treated <- function(
)
])

successfully_treated <- calculate_successful_treatments(
parameters,
seek_treatment,
drugs,
timestep,
renderer,
""
)

if (successfully_treated$successfully_treated$size() > 0) {

if(parameters$antimalarial_resistance) {
dt_update_vector <- successfully_treated$dt_spc_combined
} else {
dt_update_vector <- parameters$dt
}

update_infection(
variables$state,
'Tr',
variables$infectivity,
parameters$cd * parameters$drug_rel_c[successfully_treated$drugs],
variables$progression_rates,
1/dt_update_vector,
successfully_treated$successfully_treated
)

variables$drug$queue_update(
successfully_treated$drugs,
successfully_treated$successfully_treated
)
variables$drug_time$queue_update(
timestep,
successfully_treated$successfully_treated
)
}

successfully_treated$successfully_treated

}


#' @title Calculate successfully treated humans
#' @description
#' Sample successful treatments based on drug efficacy and antimalarial resistance
#' @param parameters model parameters
#' @param target bitset of treated humans
#' @param drugs drug index
#' @param timestep the current timestep
#' @param renderer simulation renderer
#' @param int_name the intervention name to use for rendering, use "" for frontline treatment
#' @noRd
calculate_successful_treatments <- function(
parameters,
target,
drugs,
timestep,
renderer,
int_name){

#+++ DRUG EFFICACY +++#
#+++++++++++++++++++++#
effectively_treated_index <- bernoulli_multi_p(parameters$drug_efficacy[drugs])
effectively_treated <- bitset_at(seek_treatment, effectively_treated_index)
effectively_treated <- bitset_at(target, effectively_treated_index)
drugs <- drugs[effectively_treated_index]
n_drug_efficacy_failures <- n_treat - effectively_treated$size()
renderer$render('n_drug_efficacy_failures', n_drug_efficacy_failures, timestep)
n_drug_efficacy_failures <- target$size() - effectively_treated$size()
renderer$render(paste0('n_', int_name, 'drug_efficacy_failures'), n_drug_efficacy_failures, timestep)

#+++ ANTIMALARIAL RESISTANCE +++#
#+++++++++++++++++++++++++++++++#
Expand All @@ -370,7 +430,7 @@ calculate_treated <- function(
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$render('n_early_treatment_failure', n_early_treatment_failure, timestep)
renderer$render(paste0('n_', int_name, '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]

Expand All @@ -380,51 +440,30 @@ calculate_treated <- function(
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$size(), timestep)
renderer$render(paste0('n_', int_name, '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$size(), timestep)
renderer$render(paste0('n_', int_name, 'successfully_treated'), successfully_treated$size(), timestep)
dt_slow_parasite_clearance <- dt_slow_parasite_clearance[slow_parasite_clearance_indices]

dt_spc_combined <- rep(parameters$dt, length(successfully_treated_indices))
dt_spc_combined[slow_parasite_clearance_indices] <- dt_slow_parasite_clearance

successfully_treated_list <- list(
drugs = drugs,
successfully_treated = successfully_treated,
dt_spc_combined = dt_spc_combined)

} else {

successfully_treated <- effectively_treated
renderer$render('n_successfully_treated', successfully_treated$size(), timestep)
renderer$render(paste0('n_', int_name, 'successfully_treated'), successfully_treated$size(), timestep)

successfully_treated_list <- list(
drugs = drugs,
successfully_treated = successfully_treated)

}

if (successfully_treated$size() > 0) {
variables$state$queue_update("Tr", successfully_treated)
variables$infectivity$queue_update(
parameters$cd * parameters$drug_rel_c[drugs],
successfully_treated
)
variables$drug$queue_update(
drugs,
successfully_treated
)
variables$drug_time$queue_update(
timestep,
successfully_treated
)
if(parameters$antimalarial_resistance) {
variables$recovery_rates$queue_update(
1/parameters$dt,
non_slow_parasite_clearance_individuals
)
variables$recovery_rates$queue_update(
1/dt_slow_parasite_clearance,
slow_parasite_clearance_individuals
)
} else {
variables$recovery_rates$queue_update(
1/parameters$dt,
successfully_treated
)
}
}

successfully_treated

successfully_treated_list
}

#' @title Schedule infections
Expand All @@ -444,6 +483,7 @@ schedule_infections <- function(
parameters,
timestep
) {

included <- treated$not(TRUE)

to_infect <- clinical_infections$and(included)
Expand All @@ -457,7 +497,7 @@ schedule_infections <- function(
'D',
variables$infectivity,
parameters$cd,
variables$recovery_rates,
variables$progression_rates,
1/parameters$dd,
to_infect
)
Expand Down
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