Usage (Log10 Uniform Inflow)

How to work with the kwb.qmra package in R(Studio) is described in the following chapters

Once the R package is installed it can be loaded with the following command in R(Studio):

library(kwb.qmra)

1 Input data

1.1 Location of ‘dummy’ configuration

The folder with the csv configuration files for a hypothetical use case is located here:

#### DEFINE DIRECTORY ################
confDir <- system.file("extdata/configs/dummy_log10_uniform", package = "kwb.qmra")
confDir

## [1] "/Users/runner/work/_temp/Library/kwb.qmra/extdata/configs/dummy_log10_uniform"

The following screenshot shows the required configuration files located in confDir (here: /Users/runner/work/_temp/Library/kwb.qmra/extdata/configs/dummy_log10_uniform):

Screenshot of required configuration files

1.2 Import configuration into R

All csv files with the input data for the hypothetical u dummy (as shown above) are imported into R with the following function:

#### LOAD ############################
config <- config_read(confDir)

2 Check input data

The QMRA will be performed - in case the user does not modify them in R - based on the imported input data, which are defined in the configuration folder.

In case of the dummy configuration, a Monte carlo simulation (n = 10) for 365 exposure events per year for three pathogens and the following input parameters will be performed:

Simulated pathogens for QMRA (defined in: ‘inflow.csv’ with simulated = 1):
PathogenID	PathogenName	PathogenGroup
3	Campylobacter jejuni and Campylobacter coli	Bacteria
32	Rotavirus	Viruses
36	Giardia duodenalis	Protozoa

Inflow concentrations (defined in: ‘inflow.csv’) for pathogens used for QMRA:
PathogenID	PathogenName	PathogenGroup	type	min	max
3	Campylobacter jejuni and Campylobacter coli	Bacteria	log10_uniform	10	10000
32	Rotavirus	Viruses	log10_uniform	10	10000
36	Giardia duodenalis	Protozoa	log10_uniform	10	10000

Treatment schemes (defined in: ‘treatment_schemes.csv’) used for QMRA:
TreatmentSchemeID	TreatmentSchemeName	TreatmentID	TreatmentName
1	Berlin (BF + Slow sand)	8	Slow sand filtration
1	Berlin (BF + Slow sand)	9	Bank filtration
2	Depth & surface filtration	1	Conventional clarification
2	Depth & surface filtration	5	Granular high-rate filtration
2	Depth & surface filtration	15	UV

Treatment processes (defined in: ‘treatment_proecesses.csv’) and assumed log-reductions used for QMRA (from WHO, 2011):
TreatmentID	TreatmentName	TreatmentGroup	PathogenGroup	type	min	max
1	Conventional clarification	Coagulation, flocculation and sedimentation	Bacteria	uniform	0.20	2.0
1	Conventional clarification	Coagulation, flocculation and sedimentation	Protozoa	uniform	1.00	2.0
1	Conventional clarification	Coagulation, flocculation and sedimentation	Viruses	uniform	0.10	3.4
5	Granular high-rate filtration	Filtration	Bacteria	uniform	0.20	4.4
5	Granular high-rate filtration	Filtration	Protozoa	uniform	0.40	3.3
5	Granular high-rate filtration	Filtration	Viruses	uniform	0.00	3.5
8	Slow sand filtration	Filtration	Bacteria	uniform	2.00	6.0
8	Slow sand filtration	Filtration	Protozoa	uniform	0.30	5.0
8	Slow sand filtration	Filtration	Viruses	uniform	0.25	4.0
9	Bank filtration	Pretreatment	Bacteria	uniform	2.00	6.0
9	Bank filtration	Pretreatment	Protozoa	uniform	1.00	2.0
9	Bank filtration	Pretreatment	Viruses	uniform	2.10	8.3
15	UV	Primary disinfection	Bacteria	uniform	4.00	4.0
15	UV	Primary disinfection	Protozoa	uniform	4.00	4.0
15	UV	Primary disinfection	Viruses	uniform	4.00	4.0

Ingested volume per event (defined in: row 3 ‘volume_perEvent’ in ‘exposure.csv’) (source: own assumption)):
name	type	min	max	mode
volume_perEvent	triangle	0.5	3	1.5

Dose-response models (defined in: ‘doseresponse.csv’) used for QMRA (from QMRAwiki):
PathogenID	PathogenName	PathogenGroup	Best fit model*	k	alpha	N50	Host type	Dose units	Route	Response	Reference	Link
3	Campylobacter jejuni and Campylobacter coli	Bacteria	beta-Poisson	NA	0.144	890.00	human	CFU	oral (in milk)	infection	Black et al 1988	http://qmrawiki.canr.msu.edu/index.php/Campylobacter_jejuni_and_Campylobacter_coli:_Dose_Response_Models
32	Rotavirus	Viruses	beta-Poisson	NA	0.253	6.17	human	FFU	oral	infection	Ward et al, 1986	http://qmrawiki.canr.msu.edu/index.php/Rotavirus:_Dose_Response_Models
36	Giardia duodenalis	Protozoa	exponential	0.0199	NA	NA	human	Cysts	oral	infection	Rendtorff 1954	http://qmrawiki.canr.msu.edu/index.php/Giardia_duodenalis:_Dose_Response_Models

Health parameters (defined in: ‘health.csv’) for simulated pathogens (from WHO, 2011):
PathogenID	PathogenName	infection_to_illness	dalys_per_case
3	Campylobacter jejuni and Campylobacter coli	0.70	0.0046
32	Rotavirus	0.03	0.0140
36	Giardia duodenalis	0.30	0.0015

4 Run risk calculation

Subsequently the risk calculation can be performed in R(Studio) by executing the following code, which uses the config that was imported and inspected above:

risk <- kwb.qmra::simulate_risk(config)

## 
## # STEP 0: BASIC CONFIGURATION
## 
## Simulated 3 pathogen(s): Campylobacter jejuni and Campylobacter coli, Rotavirus, Giardia duodenalis
## Number of random distribution repeatings: 10
## Number of exposure events: 365
## 
## # STEP 1: INFLOW
## 
## Simulated pathogen: Campylobacter jejuni and Campylobacter coli
## Create 10 random distribution(s): 10^runif (n: 365, min: 1.000000, max: 4.000000)
## Simulated pathogen: Rotavirus
## Create 10 random distribution(s): 10^runif (n: 365, min: 1.000000, max: 4.000000)
## Simulated pathogen: Giardia duodenalis
## Create 10 random distribution(s): 10^runif (n: 365, min: 1.000000, max: 4.000000)
## Providing inflow events ... ok. (0.00s) 
## Providing inflow paras ... ok. (0.00s) 
## 
## # STEP 2: TREATMENT SCHEMES
## 
## Create 10 random distribution(s): uniform (n: 365, min: 0.200000, max: 2.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 0.100000, max: 3.400000)
## Create 10 random distribution(s): uniform (n: 365, min: 0.200000, max: 4.400000)
## Create 10 random distribution(s): uniform (n: 365, min: 0.400000, max: 3.300000)
## Create 10 random distribution(s): uniform (n: 365, min: 0.000000, max: 3.500000)
## Create 10 random distribution(s): uniform (n: 365, min: 2.000000, max: 6.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 0.300000, max: 5.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 0.250000, max: 4.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 2.000000, max: 6.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 2.100000, max: 8.300000)
## Create 10 random distribution(s): uniform (n: 365, min: 4.000000, max: 4.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 4.000000, max: 4.000000)
## Create 10 random distribution(s): uniform (n: 365, min: 4.000000, max: 4.000000)
## Simulated treatment: Conventional clarification for Bacteria
## Simulated treatment: Conventional clarification for Protozoa
## Simulated treatment: Conventional clarification for Viruses
## Simulated treatment: Granular high-rate filtration for Bacteria
## Simulated treatment: Granular high-rate filtration for Protozoa
## Simulated treatment: Granular high-rate filtration for Viruses
## Simulated treatment: Slow sand filtration for Bacteria
## Simulated treatment: Slow sand filtration for Protozoa
## Simulated treatment: Slow sand filtration for Viruses
## Simulated treatment: Bank filtration for Bacteria
## Simulated treatment: Bank filtration for Protozoa
## Simulated treatment: Bank filtration for Viruses
## Simulated treatment: UV for Bacteria
## Simulated treatment: UV for Protozoa
## Simulated treatment: UV for Viruses
## Simulated treatment: Conventional clarification for Bacteria
## Simulated treatment: Conventional clarification for Protozoa
## Simulated treatment: Conventional clarification for Viruses
## Simulated treatment: Granular high-rate filtration for Bacteria
## Simulated treatment: Granular high-rate filtration for Protozoa
## Simulated treatment: Granular high-rate filtration for Viruses
## Simulated treatment: Slow sand filtration for Bacteria
## Simulated treatment: Slow sand filtration for Protozoa
## Simulated treatment: Slow sand filtration for Viruses
## Simulated treatment: Bank filtration for Bacteria
## Simulated treatment: Bank filtration for Protozoa
## Simulated treatment: Bank filtration for Viruses
## Simulated treatment: UV for Bacteria
## Simulated treatment: UV for Protozoa
## Simulated treatment: UV for Viruses

## Joining, by = "TreatmentID"
## Joining, by = "TreatmentID"

## 
## # STEP 3: EXPOSURE
## 
## Simulated exposure: volume per event
## Create 10 random distribution(s): triangle (n: 365, min: 0.500000, max: 3.000000, mode = 1.500000)

## Joining, by = c("PathogenGroup", "eventID", "repeatID")
## Joining, by = c("eventID", "repeatID")

## 
## # STEP 4: DOSE RESPONSE
## 
## # A tibble: 3 × 13
##   PathogenID PathogenName   PathogenGroup `Best fit mode…`       k  alpha    N50
##        <dbl> <chr>          <chr>         <chr>              <dbl>  <dbl>  <dbl>
## 1          3 Campylobacter… Bacteria      beta-Poisson     NA       0.144 890   
## 2         32 Rotavirus      Viruses       beta-Poisson     NA       0.253   6.17
## 3         36 Giardia duode… Protozoa      exponential       0.0199 NA      NA   
## # … with 6 more variables: `Host type` <chr>, `Dose units` <chr>, Route <chr>,
## #   Response <chr>, Reference <chr>, Link <chr>
## 
## # STEP 5: HEALTH
## 
## # A tibble: 3 × 4
##   PathogenID PathogenName                        infection_to_il… dalys_per_case
##        <dbl> <chr>                                          <dbl>          <dbl>
## 1          3 Campylobacter jejuni and Campyloba…             0.7          0.0046
## 2         32 Rotavirus                                       0.03         0.014 
## 3         36 Giardia duodenalis                              0.3          0.0015

## Joining, by = c("PathogenID", "PathogenName")

All (input & output) data will be saved in the resulting R object risk which an be easily inspected by the user, e.g.:

Input data

str(risk$input, 1)

## List of 5
##  $ inflow      :List of 1
##  $ treatment   :List of 1
##  $ exposure    :List of 1
##  $ doseresponse:List of 1
##  $ health      : tibble [3 × 4] (S3: tbl_df/tbl/data.frame)

Output data

str(risk$output, 1)

## List of 4
##  $ events          : tibble [54,750 × 20] (S3: tbl_df/tbl/data.frame)
##  $ total           : grouped_df [60 × 15] (S3: grouped_df/tbl_df/tbl/data.frame)
##   ..- attr(*, "groups")= tibble [60 × 6] (S3: tbl_df/tbl/data.frame)
##   .. ..- attr(*, ".drop")= logi TRUE
##  $ stats_total     : grouped_df [54 × 14] (S3: grouped_df/tbl_df/tbl/data.frame)
##   ..- attr(*, "groups")= tibble [6 × 6] (S3: tbl_df/tbl/data.frame)
##   .. ..- attr(*, ".drop")= logi TRUE
##  $ stats_logremoval: grouped_df [15 × 13] (S3: grouped_df/tbl_df/tbl/data.frame)
##   ..- attr(*, "groups")= tibble [5 × 5] (S3: tbl_df/tbl/data.frame)
##   .. ..- attr(*, ".drop")= logi TRUE

Thus the user has access to all

5 Visualise results

Finally the results of the QMRA can be visualised for each system component as shown below:

5.1 Inflow

kwb.qmra::plot_inflow(risk)

Simulated inflow concentrations

5.2 Treatment

kwb.qmra::plot_reduction(risk)

Simulated reductions in the treatment plant

5.3 Effluent

kwb.qmra::plot_effluent(risk)

Simulated effluent concentrations

5.4 Exposure

kwb.qmra::plot_event_dose(risk)

Simulated dose per event

kwb.qmra::plot_event_volume(risk)

Simulated ingested volume per event

5.5 Health results

5.5.1 Per event

kwb.qmra::plot_event_infectionProb(risk)

Simulated infection probability per event

kwb.qmra::plot_event_illnessProb(risk)

Simulated illness probability per event

kwb.qmra::plot_event_dalys(risk)

Simulated DALYs per event

5.5.2 Total

kwb.qmra::plot_total_infectionProb(risk)

Simulated total infection probability (for all events)

kwb.qmra::plot_total_illnessProb(risk)

Simulated total illness probability (for all events

kwb.qmra::plot_total_dalys(risk)

Simulated total DALYs (for all events)

In addtion tables with summary statistics, e.g. for the total risk can be generated easily as shown below:

Total risk (for first repeat of random generation)
repeatID	TreatmentSchemeID	TreatmentSchemeName	PathogenID	PathogenName	PathogenGroup	events	inflow_median	logreduction_median	volume_sum	exposure_sum	dose_sum	infectionProb_sum	illnessProb_sum	dalys_sum
1	1	Berlin (BF + Slow sand)	3	Campylobacter jejuni and Campylobacter coli	Bacteria	730	397.6866	4.070146	1214.135	2013.628	2029	1	0.9999925	0.0511046
1	1	Berlin (BF + Slow sand)	32	Rotavirus	Viruses	730	360.8239	3.207422	1214.135	53994.717	54485	1	0.9950892	0.0737280
1	1	Berlin (BF + Slow sand)	36	Giardia duodenalis	Protozoa	730	378.3326	1.749953	1214.135	70648.982	70443	1	1.0000000	0.1040879
1	2	Depth & surface filtration	3	Campylobacter jejuni and Campylobacter coli	Bacteria	1095	397.6866	2.314288	1821.202	194963.951	195325	1	1.0000000	0.4574742
1	2	Depth & surface filtration	32	Rotavirus	Viruses	1095	360.8239	2.586683	1821.202	225747.585	225697	1	0.9999883	0.1573999
1	2	Depth & surface filtration	36	Giardia duodenalis	Protozoa	1095	378.3326	1.982351	1821.202	88824.815	89097	1	1.0000000	0.1239073

6 Export results

E.g. Write reports for all configurations in package subfolder extdata/configs/ (here: /Users/runner/work/_temp/Library/kwb.qmra/extdata/configs/):

confDirs <- system.file("extdata/configs/", package = "kwb.qmra")
kwb.qmra::report_workflow(confDirs)

Michael Rustler

09 June, 2022