Simulated data for an illustrative quantitative microbial risk assessment of Cryptosporidium infection in a water distribution system. Each row represents one distribution zone under one management scenario.
Format
A data frame with 4 rows and 15 columns:
- zone
Distribution-zone identifier.
- scenario_id
Management-scenario identifier.
"0"indicates the baseline scenario.- population
Number of people exposed in the distribution zone.
- 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, 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.
Details
The example includes a baseline scenario, identified by "0", and a
hypothetical network-maintenance scenario that reduces the daily intrusion
probability by 80 percent.
References
World Health Organization (2016). Quantitative microbial risk assessment: application for water safety management. https://www.who.int/publications/i/item/9789241565370
Messner MJ, Chappell CL, and Okhuysen PC (2001). Risk assessment for Cryptosporidium: a hierarchical Bayesian analysis of human dose-response data. Water Research, 35, 3934-3940. doi:10.1016/S0043-1354(01)00119-1
Besner MC, Prevost M, and Regli S (2011). Assessing the public health risk of microbial intrusion events in distribution systems. Water Research, 45, 961-979. doi:10.1016/j.watres.2010.10.035
Examples
water_data
#> zone scenario_id population source_conc_min source_conc_mode
#> 1 Zone A 0 500 0.5 1
#> 2 Zone B 0 1500 0.5 1
#> 3 Zone A Network maintenance 500 0.5 1
#> 4 Zone B Network maintenance 1500 0.5 1
#> source_conc_max treatment_lrv_min treatment_lrv_mode treatment_lrv_max
#> 1 1.5 2.5 3 3.5
#> 2 1.5 2.5 3 3.5
#> 3 1.5 2.5 3 3.5
#> 4 1.5 2.5 3 3.5
#> water_volume intrusion_prob intrusion_conc_min intrusion_conc_max
#> 1 1.5 1e-03 0.01 0.05
#> 2 1.5 5e-03 0.01 0.05
#> 3 1.5 2e-04 0.01 0.05
#> 4 1.5 1e-03 0.01 0.05
#> dose_response_r exposure_days
#> 1 0.018 30
#> 2 0.018 30
#> 3 0.018 30
#> 4 0.018 30