Bathing water guide: Difference between revisions
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[[Category:Contains R-code]] | [[Category:Contains R-code]] | ||
{{method|moderator:Heta}} | {{method|moderator:Heta}} | ||
[[op_fi:Uimavesiopas|Uimavesiopas]] | |||
== Question == | == Question == | ||
| Line 10: | Line 11: | ||
== Answer == | == Answer == | ||
The model below is already functional. However, there is still work to do to ensure that the input data the model uses is accurate. | |||
The contamination classes used in the model are not comparable to the bathing water classification used by the EU. To see the microbe concentrations for each of the contamination classes, see page [[Pathogen concentrations in raw water]]. | |||
<rcode | <rcode | ||
name=" | name="bathingwaterguide" | ||
label=" | label="Run model" | ||
embed=0 | embed=0 | ||
graphics=1 | graphics=1 | ||
variables=" | variables=" | ||
name:rawclass|description: | name:rawclass|description:Bathing water contamination|type:selection| | ||
options: | options: | ||
'Surface water - low contamination'; | 'Surface water - low contamination';Low contamination by waste water; | ||
'Surface water - medium contamination'; | 'Surface water - medium contamination';Medium contamination by waste water; | ||
'Surface water - high contamination'; | 'Surface water - high contamination';High contamination by waste water; | ||
'Hand input'; | 'Hand input';I will give pathogen concentrations by hand| | ||
default:'Hand input'| | default:'Hand input'| | ||
category: | category:Situation| | ||
name:total_population|default:100|description: | name:total_population|default:100|description:Beach goers per day| | ||
name:Campylo|description: | name:Campylo|description:Campylobacter concentration (microbes/l)|default:0| | ||
category: | category:Microbe concentration of bathing water| | ||
category_conditions:rawclass;'User'| | category_conditions:rawclass;'User'| | ||
name:E.coli|description:E.coli O157:H7 | name:E.coli|description:''E.coli'' O157:H7 concentration (microbes/l)|default:0| | ||
name:Rota|description: | name:Rota|description:Rotavirus concentration (microbes/l)|default:0| | ||
name:Noro|description: | name:Noro|description:Norovirus concentration (microbes/l)|default:0| | ||
name:Crypto|description: | name:Crypto|description:Cryptosporidium concentration (microbes/l)|default:0| | ||
name:Giardia|description: | name:Giardia|description:Giardia concentration (microbes/l)|default:0 | ||
" | " | ||
> | > | ||
# | # This is code "Op_en7961/bathingwaterguide" on page [[Bathing water guide]] | ||
library(OpasnetUtils) | library(OpasnetUtils) | ||
library(ggplot2) | library(ggplot2) | ||
objects.latest(" | objects.latest("Op_en7961", code_name="initialization") # [[Bathing water guide]] fetches the variables needed in the model | ||
# | # Create variables from input data | ||
RawClass <- Ovariable("RawClass", data=data.frame(RawWaterClass = rawclass, Result=1)) | RawClass <- Ovariable("RawClass", data=data.frame(RawWaterClass = rawclass, Result=1)) | ||
# | # If microbe concentrations are given by hand, they're put into an ovariable | ||
if (rawclass == "Hand input") { | if (rawclass == "Hand input") { | ||
RawConcentration <- Ovariable( | RawConcentration <- Ovariable( | ||
| Line 77: | Line 78: | ||
"population", | "population", | ||
dependencies=data.frame( | dependencies=data.frame( | ||
Name=c("total_population") | Name=c("total_population", "distribution"), | ||
Ident=c(NA, "Op_en7961/age_distribution") | |||
), | ), | ||
formula=function (...) { | formula=function (...) { | ||
return(distribution * total_population) | |||
} | } | ||
) | ) | ||
| Line 97: | Line 94: | ||
BoDattr <- EvalOutput(BoDattr, forceEval=TRUE) | BoDattr <- EvalOutput(BoDattr, forceEval=TRUE) | ||
cat(" | cat("Microbe concentrations in bathing water, microbes/liter. \n") | ||
oprint(RawConcentration@output[,c("Pathogen", "RawConcentrationResult")]) | oprint(RawConcentration@output[,c("Pathogen", "RawConcentrationResult")]) | ||
cat(" | cat("Exposure: how many of each microbe people of different ages swallow with bathing water.\n") | ||
oprint(summary(exposure)) | oprint(summary(exposure)) | ||
| Line 109: | Line 106: | ||
theme_gray(base_size=24)+ | theme_gray(base_size=24)+ | ||
labs( | labs( | ||
title=" | title="Burden of disease of bathing water microbes per day", | ||
subtitle=paste(" | subtitle=paste("on a beach of", sum(result(population)), "bathers daily"), | ||
y = " | y = "Burden of disease (DALY/day)", | ||
x = " | x = "Microbe" | ||
) | ) | ||
# | # number of cases | ||
cases <- BoDattr/case_burden | cases <- BoDattr/case_burden | ||
| Line 125: | Line 122: | ||
theme_gray(base_size=24)+ | theme_gray(base_size=24)+ | ||
labs( | labs( | ||
title=" | title="Cases of illness caused daily by bathing water microbes", | ||
subtitle=paste(" | subtitle=paste("on a beach of", sum(result(population)), "bathers daily"), | ||
y = " | y = "Cases per day", | ||
x = " | x = "Microbe" | ||
) | ) | ||
# | |||
plotdata <- oapply(unkeep(cases, sources=TRUE), NULL, mean, "Iter")@output | |||
plotdata$Age <- factor(plotdata$Age, ordered=T, levels=c("0-4", "5-9", "10-14", "15-24", "25-64", "65-79", "80+")) | |||
cat("The cases of each disease are calculated as a sum of cases of that disease caused by each pathogen. Because it is possible for an individual to be ill as a result of diseases from multiple pathogens, in the figure below the numbers of sick individuals is higher than it would be in reality. For example, all six pathogens included in the model cause gastroenteritis, so if an individual is ill with gastroenteritis caused by all six pathogens, they would be counted in the gastroenteritis cases six times. In these cases the gastroenteritis this individual has would be worse than if ill from only one pathogen. It is also possible for an individual to get one disease from one pathogen and a different disease at the same time from a different one, in which case the individual is counted multiple, once in each disease they have. \n") | |||
ggplot( | |||
plotdata, | |||
aes(x=Age, weight=Result, fill=Response)) + | |||
geom_bar() + | |||
# coord_flip() + | |||
theme_gray(base_size=24)+ | |||
labs( | |||
title="Cases of illness caused daily by bathing water microbes in different age groups", | |||
subtitle=paste("on a beach of", sum(result(population)), "bathers daily"), | |||
y = "Cases of illness daily", | |||
x = "Age" | |||
) + | |||
theme(axis.text.x = element_text(angle = 45, hjust=1)) | |||
# cases as percent of beachgoers | |||
cases_percent <- cases*100/total_population | cases_percent <- cases*100/total_population | ||
| Line 141: | Line 158: | ||
theme_gray(base_size=24)+ | theme_gray(base_size=24)+ | ||
labs( | labs( | ||
title=" | title="Proportion of bathers who get ill from bathing water microbes", | ||
subtitle=paste(" | subtitle=paste("on a beach of", sum(result(population)), "bathers daily"), | ||
y = " | y = "Percent of bathers", | ||
x = " | x = "Microbe" | ||
) | ) | ||
</rcode> | </rcode> | ||
: " | : "Run model" -button opens a new tab, on which the results will appear after the model is done running. Current running time is ~40 seconds. | ||
== Rationale == | |||
=== Dependencies === | |||
{| {{prettytable}} | |||
|+ '''Pages containing the data and codes used by the model | |||
! Page !! What data or code the page has !! What is it used for !! Other observations | |||
|---- | |||
| Op_en7961 Bathing water guide (this page) || Data on time spent in water and amount of water swallowed during bathing, as well as the age distribution of beach-goers. || The amount of water swallowed per hour is multiplied by time spent in water to find the total water swallowed per bathing event. The age distribution is used, because people of different ages swallow different amounts of water and spend different lengths of time in water. Also susceptibility to diseases and their severity changes with age, so burden of disease and cases of illness are calculated for each age group. || | |||
|---- | |||
| Op_en7953 [[Pathogen concentrations in raw water]] || The amounts of pathogens in different (raw) water classes || If the user has chosen a water contamination class, the amounts of each pathogen in water of that contamination class is found here. If the user has given the amounts of pahtogens by hand, the data on this page isn't used and the given values are used instead. || The data on this page is also used in [[Water guide]], and it is better suited to be used as data on rawa water for drinking water than for bathing water. This is alos why the classification does not match the bathing water classification used by the EU. | |||
|---- | |||
| Op_en7947 [[Case burden of waterborne microbes]] || Case burdens of different pathogens, or how severe and long-lasting the diseases caused by them are || This data is used to calculate the health detriments caused by the pathogens in bathing water. || | |||
|---- | |||
| Op_en7957 [[Health impacts of waterborne microbes]] || Doesn't contain data, only code || These codes use the amount of water swallowed and the pathogen concentrations in bathing water to calculate the exposure to pathogens experienced by one individual, or how many of each pathogen an individual swallows during a swimming event. | | |||
|---- | |||
| Op_en7948 [[ERF of waterborne microbes]] || Describes the exposure-response functions for the pathogens || These functions tell how likely an individual is to get sick with different exposures. This and the exposure are used to estimate the cases of illness. || | |||
|---- | |||
| Op_en2261 [[HIA]] || Code that combines all of the above || The code on the page uses exposure, exposure-response functions and the case burdens to calculate the total burdens caused by pathogens in bathing water. From here it is also possible to calculate the number of people getting ill from bathing water. These are the final results of the model. || | |||
|} | |||
=== Data === | === Data === | ||
''' | '''Amount of water swallowed, ml/h''' | ||
{| {{prettytable}} | {| {{prettytable}} | ||
! | ! Reference !! Adults !! Children !! Notes | ||
|---- | |---- | ||
| Dufour et al. 2006 <ref> Dufour et al. 2006. Water ingestion during swimming activities in a pool: a pilot study. J Water Health (2006) 4 (4): 425-430. [https://doi.org/10.2166/wh.2006.0026] </ref> | | Dufour et al. 2006 <ref> Dufour et al. 2006. Water ingestion during swimming activities in a pool: a pilot study. J Water Health (2006) 4 (4): 425-430. [https://doi.org/10.2166/wh.2006.0026] </ref> | ||
| 0 - 70,67 ( | | 0 - 70,67 (avg 21,33) | ||
| 0 - 205,33 ( | | 0 - 205,33 (avg 49,33) | ||
| | | They measured cleaning chemicals added to the pool water from the bathers' urine | ||
|---- | |---- | ||
| Dufour et al. 2017 <ref> Dufour et al. 2017. Ingestion of swimming pool water by recreational swimmers. J Water Health (2017) 15 (3): 429-437. [https://doi.org/10.2166/wh.2017.255]</ref> | | Dufour et al. 2017 <ref> Dufour et al. 2017. Ingestion of swimming pool water by recreational swimmers. J Water Health (2017) 15 (3): 429-437. [https://doi.org/10.2166/wh.2017.255]</ref> | ||
| 12,4 (CI95 11-14) | | 12,4 (CI95 11-14) | ||
| 6-10 | | 6-10-year-olds: 23,9 (CI95 17-33) | ||
11-15 vuotiaat: 23,7 (CI95 19-30) | 11-15 vuotiaat: 23,7 (CI95 19-30) | ||
| | | Same as previous, but with a bigger sample size | ||
|---- | |---- | ||
| Suppes et al. 2013 <ref> Suppes et al. 2013. Assessment of swimmer behaviors on pool water ingestion. J Water Health (2013) 12 (2): 269-279. [https://doi.org/10.2166/wh.2013.123]</ref> | | Suppes et al. 2013 <ref> Suppes et al. 2013. Assessment of swimmer behaviors on pool water ingestion. J Water Health (2013) 12 (2): 269-279. [https://doi.org/10.2166/wh.2013.123]</ref> | ||
| 0 - 50,9 ( | | 0 - 50,9 (avg 3,5) | ||
| 0,9 - 105,5 ( | | 0,9 - 105,5 (avg 25,7) | ||
| | | Also measuring chemicals in urine | ||
|---- | |---- | ||
| Suppes et al. 2016 <ref> Suppes et al. 2016. Cryptosporidium risk from swimming pool exposures. International Journal of Hygiene and Environmental Health 219(8):915-919 [https://doi.org/10.1016/j.ijheh.2016.07.001]</ref> | | Suppes et al. 2016 <ref> Suppes et al. 2016. Cryptosporidium risk from swimming pool exposures. International Journal of Hygiene and Environmental Health 219(8):915-919 [https://doi.org/10.1016/j.ijheh.2016.07.001]</ref> | ||
| 0 - 60,6 ( | | 0 - 60,6 (avg 6,3) | ||
| 0 - 105,5 ( | | 0 - 105,5 (avg 24,2) | ||
| | | Same methods as the previous one | ||
|--- | |--- | ||
| Schets et al. 2011 <ref> Schets et al. 2011. Exposure assessment for swimmers in bathing waters and swimming pools. Water Research 45:2392-2400. [https://doi.org/10.1016/j.watres.2011.01.025]</ref> | | Schets et al. 2011 <ref> Schets et al. 2011. Exposure assessment for swimmers in bathing waters and swimming pools. Water Research 45:2392-2400. [https://doi.org/10.1016/j.watres.2011.01.025]</ref> | ||
| 20 - 30 | | 20 - 30 | ||
| 28 | | 28 | ||
| | | Questionnaire. These values are for fresh water, but the study also looked at values for sea and pool water. Values are averages (20 for women, 30 for men). | ||
|---- | |---- | ||
| DeFlorio-Barker et al. 2018 <ref> DeFlorio-Barker et al. 2018. Child environmental exposures to water and sand at the beach: Findings from studies of over 68,000 subjects at 12 beaches. Journal of Exposure Science and Environmental Epidemiology 28:93–100 [https://www.nature.com/articles/jes201723]</ref> | | DeFlorio-Barker et al. 2018 <ref> DeFlorio-Barker et al. 2018. Child environmental exposures to water and sand at the beach: Findings from studies of over 68,000 subjects at 12 beaches. Journal of Exposure Science and Environmental Epidemiology 28:93–100 [https://www.nature.com/articles/jes201723]</ref> | ||
| n. 0 - 114 (keskiarvo n. 16) | | n. 0 - 114 (keskiarvo n. 16) | ||
| n. 0.7 - 144 (keskiarvo n. 27) | | n. 0.7 - 144 (keskiarvo n. 27) | ||
| | | Quistionnaire, simulations of amounts of swallowed water based on behavior and Dufour (2017) values. | ||
|} | |} | ||
''' | '''Time spent in water (min) | ||
{| {{prettytable}} | {| {{prettytable}} | ||
! | ! Reference !! Adults !! Children !! Notes | ||
|---- | |---- | ||
| Dufour et al. 2017 | | Dufour et al. 2017 | ||
| 50,3 (CI95 49-52) | | 50,3 (CI95 49-52) | ||
| 6-10 | | 6-10-year.olds: 95,9 (CI95 88-104) | ||
11-15 | 11-15-year-olds: 55,8 (CI95 55-59) | ||
| | | The study participants were asked to stay in the water for approximately one hour | ||
|---- | |---- | ||
| Suppes et al. 2016 | | Suppes et al. 2016 | ||
| 20 - 240 ( | | 20 - 240 (avg 72) | ||
| 30 - 480 ( | | 30 - 480 (avg 114) | ||
| | | | ||
|---- | |---- | ||
| Line 213: | Line 250: | ||
|---- | |---- | ||
| DeFlorio-Barker et al. 2018 | | DeFlorio-Barker et al. 2018 | ||
| 1-360 ( | | 1-360 (avg 45,4-47) | ||
| 0-3 | | 0-3-year-olds: 2 - 300 (avg 56-66,7) | ||
4-18 | 4-18-year-olds: 1 - 360 (avg 64-93) | ||
| | | | ||
|} | |} | ||
The data for the tables below is from DeFlorio-Barker et al. 2018, and is the data the model uses. | |||
<t2b name=" | <t2b name="Swallowed water" index = "Age" unit = "l/h"> | ||
0-4|0.024(0.0005-0.087) | |||
5-9|0.024(0.0005-0.087) | |||
10-14|0.027(0.0005-0.113) | |||
15-24|0.020(0.001-0.112) | |||
25-64|0.014(0.0004-0.100) | |||
65-79|0.013(0.0004-0.115) | |||
80+|0.013(0.0004-0.115) | |||
</t2b> | </t2b> | ||
<t2b name=" | <t2b name="Bathing time" index = "Age" unit = "h"> | ||
0-4|0.5-1.5 | |||
5-9|0.75-2 | |||
10-14|0.75-2 | |||
15-24|0.5-2 | |||
25-64|0.5-1.5 | |||
65-79|0.5-1.5 | |||
80+|0.5-1.5 | |||
</t2b> | </t2b> | ||
The table below shows an estimate of the age distribution of beachgoers. The data in the table is provided by Finnish Swimming Teaching and Lifesaving Federation, and is a very, very rough estimate of the age distribution. No data is currently collected of the age distribution of beachgoers. | |||
age | |||
age | |||
age | |||
Some arguments for this distribution: | |||
*0-4-year-olds only go to the beach with 25-64-year-olds (also to a much lesser extent with the 65-79-year-olds). | |||
*25-64-year-olds cover 40 years, so even though this is a group of people working and spending time at summer cottages, the total proportion is probably 0.2 | |||
*Due to the lack of any real statistics, there is nothing to suggest differences in proportions between the age groups covering 5-64-year-olds. | |||
*The children and young adults between ages 5-24 might well cover 60% of the beachgoers, also depending on any possible activities on the beach, such as a tower with diving platforms. | |||
<t2b name="Beachgoers' age distribution" index = "Age" unit="-"> | |||
0-4|0.1 | |||
5-9|0.2 | |||
10-14|0.2 | |||
15-24|0.2 | |||
25-64|0.2 | |||
65-79|0.08 | |||
80+|0.02 | |||
</t2b> | |||
=== | === Calculations === | ||
<rcode | <rcode | ||
name=" | name="swallowed_water" | ||
embed=1 | embed=1 | ||
label=" | label="Initialize the amount of water swallowed" | ||
> | > | ||
# | # This is code "Op_en7961/swallowed_water" on page [[Bathing water guide]] | ||
library(OpasnetUtils) | library(OpasnetUtils) | ||
swallowedwater <- Ovariable("swallowedwater", ddata="Op_en7961", subset="Swallowed water") | |||
bathingtime <- Ovariable("bathingtime", ddata="Op_en7961", subset="Bathing time") | |||
Consumption <- Ovariable( | Consumption <- Ovariable( | ||
"Consumption", | "Consumption", | ||
Dependencies=data.frame( | Dependencies=data.frame( | ||
Names=c(" | Names=c("bathingtime", "swallowedwater")), | ||
formula=function(...){ | formula=function(...){ | ||
return( | return(bathingtime*swallowedwater) | ||
} | } | ||
) | ) | ||
objects.store(Consumption, | objects.store(Consumption, swallowedwater, bathingtime) | ||
#oprint(EvalOutput(Consumption)@output, show_all=TRUE) | #oprint(EvalOutput(Consumption)@output, show_all=TRUE) | ||
cat(" | cat("Ovariables Consumption, swallowedwater and bathingtime saved. \n") | ||
</rcode> | </rcode> | ||
<rcode | |||
name="age_distribution" | |||
embed=1 | |||
label="Initialize age distribution" | |||
> | |||
# This is code "Op_en7961/age_distibution" on page [[Bathing water guide]] | |||
library(OpasnetUtils) | |||
distribution <- Ovariable("distribution", ddata="Op_en7961", subset="Beachgoers' age distribution") | |||
objects.store(distribution) | |||
cat("Ovariable distribution saved. \n") | |||
</rcode> | |||
<rcode | <rcode | ||
name=" | name="initialization" | ||
label=" | label="Initialize bathing water model" | ||
embed=0 | embed=0 | ||
graphics=1 | graphics=1 | ||
> | > | ||
# This is code | # This is code Op_en7961/initialization on page [[Bathing water guide]] | ||
library(OpasnetUtils) | library(OpasnetUtils) | ||
library(ggplot2) | library(ggplot2) | ||
| Line 316: | Line 367: | ||
dependencies=data.frame( | dependencies=data.frame( | ||
Name=c("Consumption", "RawConcentration"), | Name=c("Consumption", "RawConcentration"), | ||
Ident=c(" | Ident=c("Op_en7961/swallowed_water", "Op_en7953/variable") | ||
), | ), | ||
formula = function(...) { | formula = function(...) { | ||
| Line 369: | Line 420: | ||
</rcode> | </rcode> | ||
== | ====Plotly graphs. Code doesn't work in Opasnet==== | ||
<rcode | |||
name = plotly | |||
label = "Run on own computer"> | |||
# This is code Op_en7961/plotly on page [[Bathing water guide]] | |||
library(OpasnetUtils) | |||
library(plotly) | |||
library(stringr) | |||
openv.setN(100) | |||
## Datan valmistelu ---- | |||
objects.latest("Op_en7961", code_name="initialization") # [[Uimavesiopas]] hakee koko mallin muuttujat | |||
rawclass <- c("Surface water - low contamination", | |||
"Surface water - medium contamination", | |||
"Surface water - high contamination") | |||
total_population <- Ovariable("total_population", | |||
data=data.frame(Pop = seq(50,500,50), | |||
Result = seq(50,500,50))) | |||
labels <- c("Low contamination", "Medium contamination", "High contamination") | |||
population <- Ovariable( | |||
"population", | |||
dependencies=data.frame( | |||
Name=c("total_population", "distribution"), | |||
Ident=c(NA, "Op_en7961/age_distribution") | |||
), | |||
formula=function (...) { | |||
return(distribution * total_population) | |||
} | |||
) | |||
uimavesidata <- data.frame(matrix(ncol=7, nrow=0)) | |||
colnames(uimavesidata) <- c("Response", "Exposure_agent", "Pop", "RawWaterClass", "BoDattrResult", "Result", "label") | |||
for(i in 1:length(rawclass)) { | |||
RawClass <- Ovariable("RawClass", data=data.frame(RawWaterClass = rawclass[i], Result=1)) | |||
BoDattr <- EvalOutput(BoDattr, forceEval=TRUE) | |||
cases <- BoDattr/case_burden | |||
cases <- oapply(cases, c("Response","Exposure_agent","Pop","RawWaterClass", "Iter", "BoDattrResult"), sum) | |||
cases <- aggregate(cases@output[,c("BoDattrResult", "Result")], by=cases@output[,c("Response", "Exposure_agent", "Pop", "RawWaterClass")], FUN=mean) | |||
uimavesidata <- rbind(uimavesidata, data.frame(cases, label = labels[i])) | |||
} | |||
uimavesidata$proportion <- uimavesidata$Result/uimavesidata$Pop | |||
uimavesidata$Pop <- as.factor(uimavesidata$Pop) | |||
uimavesidata$visible <- F | |||
uimavesidata$visible[uimavesidata$Pop==levels(uimavesidata$Pop)[1]] = T # smallest population visible | |||
uimavesidata <- uimavesidata[uimavesidata$Response == "gastroenteritis",] | |||
colnames(uimavesidata)[colnames(uimavesidata) == "BoDattrResult"] <- "DALY" | |||
colnames(uimavesidata)[colnames(uimavesidata) == "Result"] <- "cases" | |||
# | |||
## RANNAN KOKO + RAW CLASS MUUTTUJINA ---- | |||
# create steps and plot all traces | |||
beach_size <- list() | |||
uimavesi_plot <- plot_ly() | |||
for (i in 1:length(unique(uimavesidata$Pop))) { | |||
uimavesi_plot <- add_trace(uimavesi_plot, # add traces to uimavesi_plot | |||
type = "scatter", | |||
x=uimavesidata$cases[uimavesidata$Pop==levels(uimavesidata$Pop)[i]], # x (BoD) on x | |||
y=uimavesidata$Exposure_agent[uimavesidata$Pop==levels(uimavesidata$Pop)[i]], # y (exposure agent) on y | |||
visible = uimavesidata$visible[uimavesidata$Pop==levels(uimavesidata$Pop)[i]], # is this trace visible | |||
mode="markers", | |||
marker = list(size=10, # marker size | |||
colorscale = list(list(0, 'rgba(200,200,200,0.2)'), list(1, 'rgba(0,181,204,1)')), # sets the colors used when color=0 and color=1 | |||
color = c(rep(1, 6), # the first 17 markers use color 1 | |||
rep(0, 6*2))), | |||
hoverinfo = "x", # what is the info shown when mouse on top of marker | |||
showlegend=F) | |||
bw <- list(args = list('visible', rep(FALSE, length(unique(uimavesidata$Pop)))), # set all steps to not visible | |||
method = 'restyle', | |||
label= levels(uimavesidata$Pop)[i]) # Sets the text label to appear on the menu | |||
bw$args[[2]][i] = TRUE # set data on i step to be visible when step i is chosen | |||
beach_size[[i]] = bw # make a list with all the steps | |||
} | |||
water_qlty <- list(list(args = list('marker.color', # marker color is changed | |||
array(c(rep(1, 6), rep(0, 6*2)), dim=c(1, 18))), # these are the new markers with color 1 and 0 | |||
method = 'restyle', # restyling the markers | |||
label = "Vähäinen kuormitus"), | |||
# label = "Low contamination"), # label on the button | |||
list(args = list('marker.color', | |||
array(c(rep(0, 6), rep(1, 6), rep(0, 6)), dim=c(1, 18))), | |||
method = 'restyle', | |||
label = "Keskisuuri kuormitus"), | |||
# label = "Medium contamination"), | |||
list(args = list('marker.color', | |||
array(c(rep(0, 6*2), rep(1, 6)), dim=c(1, 18))), | |||
method = 'restyle', | |||
label = "Suuri kuormitus") | |||
# label = "High contamination") | |||
) | |||
# add slider control to plot | |||
uimavesi_plot <- uimavesi_plot %>% | |||
layout(sliders = list(list(active = 0, #Determines which button (by index starting from 0) is active. | |||
currentvalue = list(prefix = "Uimarannan kävijämäärä:"), #suomeksi | |||
# currentvalue = list(prefix = "Daily beach goers: "), # in English | |||
# the text under the graph that tells what the slider values mean | |||
steps = beach_size)), | |||
updatemenus = list(list(active = 0, | |||
y = -0.15, | |||
x = -0.1, | |||
direction = "up", | |||
buttons = water_qlty)), | |||
# title = paste("Cases of illness caused by bathing water"), # in English | |||
title = paste("Uimaveden aiheuttamat tautitapaukset päivässä"), # suomeksi | |||
xaxis = list(title ="Tautitapaukset"), | |||
yaxis = list(title ="")) | |||
uimavesi_plot | |||
# | |||
## X:N YKSIKKÖ + RAW CLASS MUUTTUJINA ---- | |||
uimavesi_leikattu <- uimavesidata[uimavesidata$Pop == 150,] | |||
nakyma <- list() | |||
uimavesiplot <- plot_ly() | |||
steps <- c("DALY", "cases", "proportion") | |||
label <- c("DALY", "tautitapaukset", "sairastumistodennäköisyys") | |||
visible <- c(T, F, F) | |||
for (i in 1:3) { | |||
uimavesiplot <- add_trace(uimavesiplot, # add traces to uimavesi_plot | |||
type = "scatter", | |||
x=uimavesi_leikattu[,colnames(uimavesi_leikattu) == steps[i]], | |||
y=uimavesi_leikattu$Exposure_agent, # y (exposure agent) on y | |||
visible = visible[i], # is this trace visible | |||
mode="markers", | |||
marker = list(size=10, # marker size | |||
colorscale = list(list(0, 'rgba(200,200,200,0.2)'), list(1, 'rgba(0,181,204,1)')), # sets the colors used when color=0 and color=1 | |||
color = c(rep(1, 6), # the first 17 markers use color 1 | |||
rep(0, 6*2))), | |||
hoverinfo = "x", # what is the info shown when mouse on top of marker | |||
showlegend=F) | |||
bw <- list(args = list('visible', rep(FALSE, 3)), # set all steps to not visible | |||
method = 'restyle', | |||
label= label[i]) # Sets the text label to appear on the menu | |||
bw$args[[2]][i] = TRUE # set data on i step to be visible when step i is chosen | |||
nakyma[[i]] = bw # make a list with all the steps | |||
} | |||
water_qlty <- list(list(args = list('marker.color', # marker color is changed | |||
array(c(rep(1, 6), rep(0, 6*2)), dim=c(1, 18))), # these are the new markers with color 1 and 0 | |||
method = 'restyle', # restyling the markers | |||
label = "Vähäinen kuormitus"), | |||
# label = "Low contamination"), # label on the button | |||
list(args = list('marker.color', | |||
array(c(rep(0, 6), rep(1, 6), rep(0, 6)), dim=c(1, 18))), | |||
method = 'restyle', | |||
label = "Keskisuuri kuormitus"), | |||
# label = "Medium contamination"), | |||
list(args = list('marker.color', | |||
array(c(rep(0, 6*2), rep(1, 6)), dim=c(1, 18))), | |||
method = 'restyle', | |||
label = "Suuri kuormitus") | |||
# label = "High contamination") | |||
) | |||
# add slider control to plot | |||
uimavesiplot <- uimavesiplot %>% | |||
layout(updatemenus = list(list(active = 0, | |||
y = 1, | |||
x = -0.2, | |||
buttons = water_qlty), | |||
list(active = 0, #Determines which button (by index starting from 0) is active. | |||
y = 0.85, | |||
x = -0.2, | |||
buttons = nakyma)), | |||
# title = paste("Cases of illness caused by bathing water on a beach of 150 dialy visitors"), # in English | |||
title = "Uimaveden mikrobien aiheuttama terveyshaitta päivässä 150 kävijän rannalla", # suomeksi | |||
# xaxis = list(title ="Tautitapaukset"), | |||
yaxis = list(title ="")) | |||
uimavesiplot | |||
</rcode> | |||
== See also == | |||
* [[ | * [[Water guide]] | ||
* EU- | * Quality classification of bathing waters based on an EU-directive [https://www.finlex.fi/data/sdliite/liite/5516.pdf] (An appendix of [https://www.finlex.fi/fi/laki/alkup/2008/20080177|a decree] of the ministry of social affairs and health) | ||
* A Randomized Controlled Trial Assessing Infectious Disease Risks from Bathing in Fresh Recreational Waters in Relation to the Concentration of Escherichia coli, Intestinal Enterococci, Clostridium perfringens, and Somatic Coliphages [https://doi.org/10.1289/ehp.8115] | * A Randomized Controlled Trial Assessing Infectious Disease Risks from Bathing in Fresh Recreational Waters in Relation to the Concentration of Escherichia coli, Intestinal Enterococci, Clostridium perfringens, and Somatic Coliphages [https://doi.org/10.1289/ehp.8115] | ||
* Detection of Cryptosporidium, Giardia and Enterocytozoon bieneusi in surface water, including recreational areas: a one-year prospective study [https://doi.org/10.1111/j.1574-695X.2006.00098.x] | * Detection of Cryptosporidium, Giardia and Enterocytozoon bieneusi in surface water, including recreational areas: a one-year prospective study [https://doi.org/10.1111/j.1574-695X.2006.00098.x] | ||
* WHO: [https://apps.who.int/iris/bitstream/handle/10665/246195/9789241565370-eng.pdf?sequence=1 Quantitative Microbial Risk Assessment: Application for Water Safety Management] | |||
== Viitteet == | == Viitteet == | ||
<references/> | <references/> | ||
Latest revision as of 07:00, 21 September 2019
| [show] |
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Question
How to asses the microbiological risks of natural bathing waters, and the health effects they have? Data on the water and bathing behaviour must be possible to give to the model as input.
Answer
The model below is already functional. However, there is still work to do to ensure that the input data the model uses is accurate.
The contamination classes used in the model are not comparable to the bathing water classification used by the EU. To see the microbe concentrations for each of the contamination classes, see page Pathogen concentrations in raw water.
- "Run model" -button opens a new tab, on which the results will appear after the model is done running. Current running time is ~40 seconds.
Rationale
Dependencies
| Page | What data or code the page has | What is it used for | Other observations |
|---|---|---|---|
| Op_en7961 Bathing water guide (this page) | Data on time spent in water and amount of water swallowed during bathing, as well as the age distribution of beach-goers. | The amount of water swallowed per hour is multiplied by time spent in water to find the total water swallowed per bathing event. The age distribution is used, because people of different ages swallow different amounts of water and spend different lengths of time in water. Also susceptibility to diseases and their severity changes with age, so burden of disease and cases of illness are calculated for each age group. | |
| Op_en7953 Pathogen concentrations in raw water | The amounts of pathogens in different (raw) water classes | If the user has chosen a water contamination class, the amounts of each pathogen in water of that contamination class is found here. If the user has given the amounts of pahtogens by hand, the data on this page isn't used and the given values are used instead. | The data on this page is also used in Water guide, and it is better suited to be used as data on rawa water for drinking water than for bathing water. This is alos why the classification does not match the bathing water classification used by the EU. |
| Op_en7947 Case burden of waterborne microbes | Case burdens of different pathogens, or how severe and long-lasting the diseases caused by them are | This data is used to calculate the health detriments caused by the pathogens in bathing water. | |
| Op_en7957 Health impacts of waterborne microbes | Doesn't contain data, only code | ||
| Op_en7948 ERF of waterborne microbes | Describes the exposure-response functions for the pathogens | These functions tell how likely an individual is to get sick with different exposures. This and the exposure are used to estimate the cases of illness. | |
| Op_en2261 HIA | Code that combines all of the above | The code on the page uses exposure, exposure-response functions and the case burdens to calculate the total burdens caused by pathogens in bathing water. From here it is also possible to calculate the number of people getting ill from bathing water. These are the final results of the model. |
Data
Amount of water swallowed, ml/h
| Reference | Adults | Children | Notes |
|---|---|---|---|
| Dufour et al. 2006 [1] | 0 - 70,67 (avg 21,33) | 0 - 205,33 (avg 49,33) | They measured cleaning chemicals added to the pool water from the bathers' urine |
| Dufour et al. 2017 [2] | 12,4 (CI95 11-14) | 6-10-year-olds: 23,9 (CI95 17-33)
11-15 vuotiaat: 23,7 (CI95 19-30) |
Same as previous, but with a bigger sample size |
| Suppes et al. 2013 [3] | 0 - 50,9 (avg 3,5) | 0,9 - 105,5 (avg 25,7) | Also measuring chemicals in urine |
| Suppes et al. 2016 [4] | 0 - 60,6 (avg 6,3) | 0 - 105,5 (avg 24,2) | Same methods as the previous one |
| Schets et al. 2011 [5] | 20 - 30 | 28 | Questionnaire. These values are for fresh water, but the study also looked at values for sea and pool water. Values are averages (20 for women, 30 for men). |
| DeFlorio-Barker et al. 2018 [6] | n. 0 - 114 (keskiarvo n. 16) | n. 0.7 - 144 (keskiarvo n. 27) | Quistionnaire, simulations of amounts of swallowed water based on behavior and Dufour (2017) values. |
Time spent in water (min)
| Reference | Adults | Children | Notes |
|---|---|---|---|
| Dufour et al. 2017 | 50,3 (CI95 49-52) | 6-10-year.olds: 95,9 (CI95 88-104)
11-15-year-olds: 55,8 (CI95 55-59) |
The study participants were asked to stay in the water for approximately one hour |
| Suppes et al. 2016 | 20 - 240 (avg 72) | 30 - 480 (avg 114) | |
| Schets et al. 2011 | 54 (CI95 6-220) | 79 | |
| DeFlorio-Barker et al. 2018 | 1-360 (avg 45,4-47) | 0-3-year-olds: 2 - 300 (avg 56-66,7)
4-18-year-olds: 1 - 360 (avg 64-93) |
The data for the tables below is from DeFlorio-Barker et al. 2018, and is the data the model uses.
| Obs | Age | Result |
|---|---|---|
| 1 | 0-4 | 0.024(0.0005-0.087) |
| 2 | 5-9 | 0.024(0.0005-0.087) |
| 3 | 10-14 | 0.027(0.0005-0.113) |
| 4 | 15-24 | 0.020(0.001-0.112) |
| 5 | 25-64 | 0.014(0.0004-0.100) |
| 6 | 65-79 | 0.013(0.0004-0.115) |
| 7 | 80+ | 0.013(0.0004-0.115) |
| Obs | Age | Result |
|---|---|---|
| 1 | 0-4 | 0.5-1.5 |
| 2 | 5-9 | 0.75-2 |
| 3 | 10-14 | 0.75-2 |
| 4 | 15-24 | 0.5-2 |
| 5 | 25-64 | 0.5-1.5 |
| 6 | 65-79 | 0.5-1.5 |
| 7 | 80+ | 0.5-1.5 |
The table below shows an estimate of the age distribution of beachgoers. The data in the table is provided by Finnish Swimming Teaching and Lifesaving Federation, and is a very, very rough estimate of the age distribution. No data is currently collected of the age distribution of beachgoers.
Some arguments for this distribution:
- 0-4-year-olds only go to the beach with 25-64-year-olds (also to a much lesser extent with the 65-79-year-olds).
- 25-64-year-olds cover 40 years, so even though this is a group of people working and spending time at summer cottages, the total proportion is probably 0.2
- Due to the lack of any real statistics, there is nothing to suggest differences in proportions between the age groups covering 5-64-year-olds.
- The children and young adults between ages 5-24 might well cover 60% of the beachgoers, also depending on any possible activities on the beach, such as a tower with diving platforms.
| Obs | Age | Result |
|---|---|---|
| 1 | 0-4 | 0.1 |
| 2 | 5-9 | 0.2 |
| 3 | 10-14 | 0.2 |
| 4 | 15-24 | 0.2 |
| 5 | 25-64 | 0.2 |
| 6 | 65-79 | 0.08 |
| 7 | 80+ | 0.02 |
Calculations
Plotly graphs. Code doesn't work in Opasnet
See also
- Water guide
- Quality classification of bathing waters based on an EU-directive [7] (An appendix of decree of the ministry of social affairs and health)
- A Randomized Controlled Trial Assessing Infectious Disease Risks from Bathing in Fresh Recreational Waters in Relation to the Concentration of Escherichia coli, Intestinal Enterococci, Clostridium perfringens, and Somatic Coliphages [8]
- Detection of Cryptosporidium, Giardia and Enterocytozoon bieneusi in surface water, including recreational areas: a one-year prospective study [9]
- WHO: Quantitative Microbial Risk Assessment: Application for Water Safety Management
Viitteet
- ↑ Dufour et al. 2006. Water ingestion during swimming activities in a pool: a pilot study. J Water Health (2006) 4 (4): 425-430. [1]
- ↑ Dufour et al. 2017. Ingestion of swimming pool water by recreational swimmers. J Water Health (2017) 15 (3): 429-437. [2]
- ↑ Suppes et al. 2013. Assessment of swimmer behaviors on pool water ingestion. J Water Health (2013) 12 (2): 269-279. [3]
- ↑ Suppes et al. 2016. Cryptosporidium risk from swimming pool exposures. International Journal of Hygiene and Environmental Health 219(8):915-919 [4]
- ↑ Schets et al. 2011. Exposure assessment for swimmers in bathing waters and swimming pools. Water Research 45:2392-2400. [5]
- ↑ DeFlorio-Barker et al. 2018. Child environmental exposures to water and sand at the beach: Findings from studies of over 68,000 subjects at 12 beaches. Journal of Exposure Science and Environmental Epidemiology 28:93–100 [6]