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Inflow concentration custom

inflow_concentration_custom.Rd

To calculate inflow concentration for specified pathogen

Usage

inflow_concentration_custom(scenario, concentration, monteCarlo = 1000)

Arguments

scenario

list scenario created with create_scenario

concentration

data.frame min, max and distribution law of pathogen concentration

monteCarlo

integer number of simulation iterations for Monte Carlo

Value

scenario_with_concentration

Examples

sc <- create_scenario(system.file("input_1culture_2pop.xlsx", package = "ambre"))
sc  <- update_volume_with_desired_value(sc, 
                                        volume = data.frame(min = c(0.5,1), 
                                                            max = c(1,2), 
                                                            type = c("triangle", "triangle")))
  
sc <- update_frequency_with_desired_value(scenario = sc, frequency = c(200L, 300L))

concentration_custom <- data.frame(PathogenName = c("Campylobacter jejuni", "Norovirus"),
                                   min = c(1, 10),
                                   max = c(2,20),
                                   type = c("uniform", "uniform"))
  
inflow_concentration_custom(scenario = sc,
                            concentration = concentration_custom)
#> Simulated pathogen: Campylobacter jejuni
#> Create 1000 random distribution(s): uniform (n: 200, min: 1.000000, max: 2.000000)
#> Simulated pathogen: Norovirus
#> Create 1000 random distribution(s): uniform (n: 200, min: 10.000000, max: 20.000000)
#> Providing inflow events ... ok. (0.03 secs) 
#> Providing inflow paras ... ok. (0.00 secs) 
#> Simulated pathogen: Campylobacter jejuni
#> Create 1000 random distribution(s): uniform (n: 300, min: 1.000000, max: 2.000000)
#> Simulated pathogen: Norovirus
#> Create 1000 random distribution(s): uniform (n: 300, min: 10.000000, max: 20.000000)
#> Providing inflow events ... ok. (0.24 secs) 
#> Providing inflow paras ... ok. (0.00 secs) 
#> # A tibble: 2 × 25
#>   CropName   Area PopulationName    nb_population PathName     STEPtreatmentName
#>   <chr>     <dbl> <chr>                     <dbl> <chr>        <chr>            
#> 1 Tomato       10 Irrigation staff              1 Ingestion o… Q.1 - Activated …
#> 2 Corn seed    35 Maintenance staff             1 Ingestion f… Q.1 - Activated …
#> # ℹ 19 more variables: CollectiveTreatmentName <chr>, InitialProcessName <chr>,
#> #   SupplementaryProcessName <chr>, nb_day_decay <dbl>, config <list>,
#> #   CropID <dbl>, CropHeight <dbl>, PopulationID <dbl>, PathID <dbl>,
#> #   MatrixID <dbl>, STEPtreatmentID <list>, CollectiveTreatmentID <list>,
#> #   InitialProcessID <list>, SupplementaryProcessID <list>,
#> #   InitialTrainName <list>, InitialTrainID <list>,
#> #   SupplementaryTrainName <list>, SupplementaryTrainID <list>, …