Run QMRA on addition process

Run the full QMRA workflow on additional processes

1. simulate inflow concentration, volume of exposure

2. calculate initial dose

3. simulate log reduction for barrier processes

4. calculate final dose

5. calculate infection probability, illness probability and dalys reduction

6. calculate annual total risk

Usage

run_qmra_supplementary_process(
  scenario,
  pathogen,
  regulationLog,
  regulationConcentration
)

Arguments

scenario

data.frame scenario obtained with create_scenario function

pathogen

character Name of the pathogen to simulate

regulationLog

data.frame regulation value of the log reduction to compare with simulation result

regulationConcentration

data.frame regulation value of the log reduction to compare with simulation result

Value

all_plots list of ggplot

Examples

# \donttest{
library(dplyr)
scenario <- create_scenario(system.file("input_1culture_2pop.xlsx", package = "ambre"))

regulation_reduction <- config_ambre$regulation$regulation_value |> 
  dplyr::filter(Country == "France") |>
  dplyr::select(-c(Concentration, Country, RegulationID))
regulation_concentration <- config_ambre$regulation$regulation_value |> 
  dplyr::filter(Country == "France") |>
  dplyr::select(-c(Country, RegulationID, Reduction))

run_qmra_supplementary_process(scenario = scenario, 
                               pathogen = c("Campylobacter jejuni", "Escherichia coli"),
                               regulationLog = regulation_reduction,
                               regulationConcentration = regulation_concentration)
#> Simulated pathogen: Campylobacter jejuni
#> Create 1000 random distribution(s): uniform (n: 60, min: 100.000000, max: 5000.000000)
#> Simulated pathogen: Escherichia coli
#> Create 1000 random distribution(s): uniform (n: 60, min: 10000000.000000, max: 1000000000.000000)
#> Providing inflow events ... ok. (0.01 secs) 
#> Providing inflow paras ... ok. (0.00 secs) 
#> Simulated pathogen: Campylobacter jejuni
#> Create 1000 random distribution(s): uniform (n: 48, min: 100.000000, max: 5000.000000)
#> Simulated pathogen: Escherichia coli
#> Create 1000 random distribution(s): uniform (n: 48, min: 10000000.000000, max: 1000000000.000000)
#> Providing inflow events ... ok. (0.01 secs) 
#> Providing inflow paras ... ok. (0.00 secs) 
#> Simulated exposure: volume per event
#> Distribution set from 'triangle' to 'uniform' because 'min' equals 'max'
#> Create 1000 random distribution(s): uniform (n: 60, min: 0.001000, max: 0.001000)
#> Simulated exposure: volume per event
#> Distribution set from 'triangle' to 'uniform' because 'min' equals 'max'
#> Create 1000 random distribution(s): uniform (n: 48, min: 0.001000, max: 0.001000)
#> Create 1000 random distribution(s): uniform (n: 60, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 60, min: 1.000000, max: 3.000000)
#> Create 1000 random distribution(s): uniform (n: 60, min: 5.000000, max: 7.000000)
#> Create 1000 random distribution(s): uniform (n: 60, min: 1.000000, max: 1.000000)
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: E.1.1 - Micro-sprinkler for Bacteria
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: E.1.1 - Micro-sprinkler for Bacteria
#> Joining with `by = join_by(TreatmentID)`
#> Joining with `by = join_by(TreatmentID)`
#> Create 1000 random distribution(s): uniform (n: 48, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 48, min: 1.000000, max: 3.000000)
#> Create 1000 random distribution(s): uniform (n: 48, min: 5.000000, max: 7.000000)
#> Create 1000 random distribution(s): uniform (n: 48, min: 3.000000, max: 6.000000)
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: P.5 - Natural die-off for Bacteria
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: P.5 - Natural die-off for Bacteria
#> Joining with `by = join_by(TreatmentID)`
#> Joining with `by = join_by(TreatmentID)`
#> $logreduction
#> $logreduction$Bacteria

#> 
#> 
#> $dalys
#> $dalys$`Campylobacter jejuni`

#> 
#> $dalys$`Escherichia coli`

#> 
#> 
#> $regul_matrix_log
#> 
#> $regul_matrix_concentration
#> 
# }