An expanded version of water_data for demonstrating multiple-group multilevel trials. Each distribution-zone and scenario combination is divided into groups with low, medium, and high daily water consumption.
Format
A data frame with 12 rows and 16 columns:
- zone
Distribution-zone identifier.
- consumption_group
Water-consumption group:
"Low","Medium", or"High".- scenario_id
Management-scenario identifier.
"0"denotes the baseline scenario.- population
Number of people in the consumption group. Group populations sum to the corresponding population in water_data.
- source_conc_min
Minimum Cryptosporidium concentration in source water, in oocysts per litre.
- source_conc_mode
Most likely Cryptosporidium concentration in source water, in oocysts per litre.
- source_conc_max
Maximum Cryptosporidium concentration in source water, in oocysts per litre.
- treatment_lrv_min
Minimum treatment performance, expressed as a log10 reduction value.
- treatment_lrv_mode
Most likely treatment performance, expressed as a log10 reduction value.
- treatment_lrv_max
Maximum treatment performance, expressed as a log10 reduction value.
- water_volume
Daily consumption of unboiled tap water for the consumption group, in litres per person per day.
- intrusion_prob
Daily probability that an intrusion affects the distribution zone.
- intrusion_conc_min
Minimum Cryptosporidium concentration at the tap conditional on an intrusion, in oocysts per litre.
- intrusion_conc_max
Maximum Cryptosporidium concentration at the tap conditional on an intrusion, in oocysts per litre.
- dose_response_r
Parameter of the exponential Cryptosporidium dose-response model.
- exposure_days
Number of daily exposure events in the assessment period.
Source
Derived from water_data using hypothetical population shares and water-consumption values.
Details
The consumption groups contain 25, 50, and 25 percent of the corresponding zone population and consume 1.0, 1.5, and 2.0 litres of unboiled tap water per person per day, respectively.
The consumption groups in the same distribution zone share the occurrence
or absence of a daily intrusion event. Their conditional infection
probabilities differ because water_volume differs between groups. This
structure can be evaluated with trial_totals() by using population as
the number of trials and aggregating by zone and scenario_id.
Examples
water_group_data
#> zone consumption_group scenario_id population source_conc_min
#> 1 Zone A Low 0 125 0.5
#> 2 Zone A Medium 0 250 0.5
#> 3 Zone A High 0 125 0.5
#> 4 Zone B Low 0 375 0.5
#> 5 Zone B Medium 0 750 0.5
#> 6 Zone B High 0 375 0.5
#> 7 Zone A Low Network maintenance 125 0.5
#> 8 Zone A Medium Network maintenance 250 0.5
#> 9 Zone A High Network maintenance 125 0.5
#> 10 Zone B Low Network maintenance 375 0.5
#> 11 Zone B Medium Network maintenance 750 0.5
#> 12 Zone B High Network maintenance 375 0.5
#> source_conc_mode source_conc_max treatment_lrv_min treatment_lrv_mode
#> 1 1 1.5 2.5 3
#> 2 1 1.5 2.5 3
#> 3 1 1.5 2.5 3
#> 4 1 1.5 2.5 3
#> 5 1 1.5 2.5 3
#> 6 1 1.5 2.5 3
#> 7 1 1.5 2.5 3
#> 8 1 1.5 2.5 3
#> 9 1 1.5 2.5 3
#> 10 1 1.5 2.5 3
#> 11 1 1.5 2.5 3
#> 12 1 1.5 2.5 3
#> treatment_lrv_max water_volume intrusion_prob intrusion_conc_min
#> 1 3.5 1.0 1e-03 0.01
#> 2 3.5 1.5 1e-03 0.01
#> 3 3.5 2.0 1e-03 0.01
#> 4 3.5 1.0 5e-03 0.01
#> 5 3.5 1.5 5e-03 0.01
#> 6 3.5 2.0 5e-03 0.01
#> 7 3.5 1.0 2e-04 0.01
#> 8 3.5 1.5 2e-04 0.01
#> 9 3.5 2.0 2e-04 0.01
#> 10 3.5 1.0 1e-03 0.01
#> 11 3.5 1.5 1e-03 0.01
#> 12 3.5 2.0 1e-03 0.01
#> intrusion_conc_max dose_response_r exposure_days
#> 1 0.05 0.018 30
#> 2 0.05 0.018 30
#> 3 0.05 0.018 30
#> 4 0.05 0.018 30
#> 5 0.05 0.018 30
#> 6 0.05 0.018 30
#> 7 0.05 0.018 30
#> 8 0.05 0.018 30
#> 9 0.05 0.018 30
#> 10 0.05 0.018 30
#> 11 0.05 0.018 30
#> 12 0.05 0.018 30
# Population is preserved within each zone and scenario.
aggregate(
population ~ zone + scenario_id,
data = water_group_data,
FUN = sum
)
#> zone scenario_id population
#> 1 Zone A 0 500
#> 2 Zone B 0 1500
#> 3 Zone A Network maintenance 500
#> 4 Zone B Network maintenance 1500