Difference between revisions of "Wikisym 2012 Demo"
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Juha Villman (talk | contribs) (→Water model) |
Juha Villman (talk | contribs) (→Water model) |
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| Line 166: | Line 166: | ||
name:i.raw.class|description:Water resource|type:selection| | name:i.raw.class|description:Water resource|type:selection| | ||
options: | options: | ||
| − | ' | + | 'Pohjavesi - Puhdas';Ground water - Clean; |
| − | ' | + | 'Pohjavesi - Pintavesikuormitus';Ground water - Surface water stress; |
| − | ' | + | 'Pintavesi - Vähäinen kuormitus';Surface water - Low stress; |
| − | ' | + | 'Pintavesi - Keskisuuri kuormitus;Surface water - Medium stress; |
| − | ' | + | 'Pintavesi - Suuri kuormitus';Surface water - High stress| |
category:Ground water: Pathogenic concentrations| | category:Ground water: Pathogenic concentrations| | ||
name:Kampylo|description:Cambylobacter-concentration estimation (microbe/l)|default:'Use water source specific classification'| | name:Kampylo|description:Cambylobacter-concentration estimation (microbe/l)|default:'Use water source specific classification'| | ||
Revision as of 13:50, 23 August 2012
Contents
Polygons on dynamic Google Maps
This example plots municipalities of Finland on Google Maps using data from National Land Survey of Finland.
library(OpasnetUtilsExt)
library(sorvi)
library(rgdal)
# Get the shape data of Finnish municipalities using soRvi library
data(MML)
shp <- MML[["1_milj_Shape_etrs_shape"]][["kunta1_p"]]
# Set the projection
epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
proj4string(shp)<-("+init=epsg:3047")
shp2<-spTransform(shp,epsg4326String)
# Create the KML data using the shape
out<-sapply(slot(shp2,"polygons"),function(x){kmlPolygon(x,name="name",col='#df0000aa',lwd=1,border='black',description="desc") })
data<-paste(
paste(kmlPolygon(kmlname="This will be layer name", kmldescription="<i>More info about layer here</i>")$header, collapse="\n"),
paste(unlist(out["style",]), collapse="\n"),
paste(unlist(out["content",]), collapse="\n"),
paste(kmlPolygon()$footer, collapse="\n"),
sep=''
)
# Show the KML data on Google Maps
google.show_kml_data_on_maps(data)
|
Points on dynamic Google Maps
This examples plots buildings of Kuopio on Google Maps. User can give the minimum age of buildings to plot as an input parameter.
library(rgdal)
library(RColorBrewer)
library(classInt)
library(OpasnetUtilsExt)
library(RODBC)
if (age > 190)
{
age <- 190
}
shp <- spatial_db_query(paste('SELECT * FROM kuopio_house WHERE ika >= ',age,';',sep=''))
coordinates(shp)=c("y_koord","x_koord")
plotvar<-shp@data$ika
nclr<-8
plotclr<-brewer.pal(nclr,"BuPu")
class<-classIntervals(plotvar,nclr,style="quantile")
colcode<-findColours(class,plotclr)
epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
proj4string(shp)<-("+init=epsg:3067")
shp2<-spTransform(shp,epsg4326String)
kmlname<-"Kuopio house data"
kmldescription<-"Random stuff about here"
icon<-"http://maps.google.com/mapfiles/kml/pal2/icon18.png"
name<-paste("Value: ",shp2$ika)
description <- paste("<b>Age:</b>",shp2$ika,"<br><b>Building ID:</b>",shp2$rakennustunnus)
data <- google.point_kml(shp2,kmlname,kmldescription,name,description,icon,colcode)
google.show_kml_data_on_maps(data)
|
Large quantity of points on a static Google Maps
This example plots large number of point data on static Google Maps. The map produced in this example shows the age (in years) distribution of buildings within Kuopio. User can select the number of age classes (4,6 or 8) and the type of classification.
#code goes here
library(RgoogleMaps)
library(rgdal)
library(maptools)
library(RColorBrewer)
library(classInt)
library(OpasnetUtilsExt)
shp<-readOGR('PG:host=localhost user=postgres dbname=spatial_db','kuopio_house')
plotvar<-shp@data$ika
nclr<-myclasses
plotclr<-brewer.pal(nclr,"Reds")
class<-classIntervals(plotvar,nclr,style=classtype)
colcode<-findColours(class,plotclr)
epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
proj4string(shp)<-("+init=epsg:3067")
shp2<-spTransform(shp,epsg4326String)
#get marker information for all points
mymarkers<-cbind.data.frame(lat=c(shp2@coords[,2]),lon=c(shp2@coords[,1]),color=colcode);
#get the bounding box:
bb <- qbbox(lat = mymarkers[,"lat"], lon = mymarkers[,"lon"])
#MyMap function without the "file destination" parameter
MyRmap<-function (lonR, latR, center, size = c(640, 640),
MINIMUMSIZE = FALSE, RETURNIMAGE = TRUE, GRAYSCALE = FALSE,
NEWMAP = TRUE, zoom, verbose = 1, ...)
{
if (missing(zoom))
zoom <- min(MaxZoom(latR, lonR, size))
if (missing(center)) {
lat.center <- mean(latR)
lon.center <- mean(lonR)
}
else {
lat.center <- center[1]
lon.center <- center[2]
}
if (MINIMUMSIZE) {
ll <- LatLon2XY(latR[1], lonR[1], zoom)
ur <- LatLon2XY(latR[2], lonR[2], zoom)
cr <- LatLon2XY(lat.center, lon.center, zoom)
ll.Rcoords <- Tile2R(ll, cr)
ur.Rcoords <- Tile2R(ur, cr)
if (verbose > 1) {
cat("ll:")
print(ll)
print(ll.Rcoords)
cat("ur:")
print(ur)
print(ur.Rcoords)
cat("cr:")
print(cr)
}
size[1] <- 2 * max(c(ceiling(abs(ll.Rcoords$X)), ceiling(abs(ur.Rcoords$X)))) +
1
size[2] <- 2 * max(c(ceiling(abs(ll.Rcoords$Y)), ceiling(abs(ur.Rcoords$Y)))) +
1
if (verbose)
cat("new size: ", size, "\n")
}
return(google.get_map(center = c(lat.center, lon.center), zoom = zoom,
size = size, RETURNIMAGE = RETURNIMAGE,
GRAYSCALE = GRAYSCALE, verbose = verbose, ...))
}
MyMap<-MyRmap(bb$lonR,bb$latR,maptype="mobile",scale="2")
PlotOnStaticMap(MyMap,size=c(640,640))
PlotOnStaticMap(MyMap,size=c(640,640),lat=mymarkers[,"lat"],lon=mymarkers[,"lon"],pch=19,cex=0.3,col=colcode,add=T)
legend("topleft", legend=names(attr(colcode, "table")),title="Building Age (Yr)", fill=attr(colcode, "palette"), cex=1.0, bty="y",bg="white")
|
Water model
library(OpasnetUtils)
library(xtable)
library(reshape2)
i.raw.pat.conc.val <- list(Kampylo, Ecoli, Rota, Noro, Crypto, Giardia)
#vedenkulutus
VedeKulu = data.frame(VedkulResult = VedeKulu)
InpVedkul = new("ovariable",name = "InpVedkul" , output = VedeKulu)
#Patogeenien pitoisuudet
Fetch2(data.frame(Name = "RaaPatPitLuo", Key = "AEmnj6ZNfhIHAt2X"), evaluate = TRUE)
RaaPatPitLuo@output <- RaaPatPitLuo@output[RaaPatPitLuo@output$Raakavesilähde == i.raw.class, ]
RaaPatPitLuo@output <- merge(
RaaPatPitLuo@output,
data.frame(
Patogeeni = c("Kampylobakteeri","E.coli O157:H7","Rotavirus","Norovirus","Cryptosporidium","Giardia"),
TempResult = suppressWarnings(as.numeric(i.raw.pat.conc.val))
)
)
RaaPatPitLuo@output$RaaPatPitLuoResult <- ifelse(
is.na(RaaPatPitLuo@output$TempResult),
RaaPatPitLuo@output$RaaPatPitLuoResult,
RaaPatPitLuo@output$TempResult
)
RaaPatPitLuo@output <- RaaPatPitLuo@output[,!colnames(RaaPatPitLuo@output)%in%"TempResult"]
Temp = rep(FALSE,8)
Temp[Puhdistus] = TRUE
Puhdistus = Temp
InpKloori = new("ovariable", output = data.frame(KlooriResult = KlooriAnnos))
#tehdaan InpVedKasTeh ovariable paalla olevista puhdistuksista
Fetch2(data.frame(Name = c("VedKasTeh","VedDesTeh"), Key = c("J8AofoHKjKEOuPWK","iFnLQFpUAH3QfwGz")))
VedKasTeh <- EvalOutput(VedKasTeh)
VedDesTeh <- EvalOutput(VedDesTeh, substitute = TRUE)
Puhdistus = c(Puhdistus,TRUE)
Puhdistus = data.frame(tottavaitarua = Puhdistus, Vedenpuhdistusmenetelmä = c("Perinteinen puhdistus"
,"Hyvin toimva puhdistus" , "Tehostettu puhdistus" ,"Hidas hiekkasuodatus"
,"Kalkkikivisuodatus","Aktiivihiilisuodatus" ,"UV" ,"Otsonointi","Klooraus"))
PuhdistusKasTeh = Puhdistus[1:6,]
PuhdistusDesTeh = Puhdistus[7:9,]
VedKasTeh@output = merge(VedKasTeh@output, PuhdistusKasTeh)
colnames(PuhdistusDesTeh)[colnames(PuhdistusDesTeh) == "Vedenpuhdistusmenetelmä"] = "Menetelmä"
VedDesTeh@output = merge(VedDesTeh@output, PuhdistusDesTeh)
VedKasTeh@output[,"VedKasTehResult"] = ifelse(
VedKasTeh@output[,"tottavaitarua"] == FALSE,
0,
VedKasTeh@output[,"VedKasTehResult"]
)
VedDesTeh@output[,"VedDesTehResult"] = ifelse(
VedDesTeh@output[,"tottavaitarua"] == FALSE,
0,
VedDesTeh@output[,"VedDesTehResult"]
)
VedKasTeh@output <- VedKasTeh@output[,!colnames(VedKasTeh@output) %in%c("tottavaitarua")]
VedDesTeh@output <- VedDesTeh@output[,!colnames(VedDesTeh@output) %in%c("tottavaitarua")]
Fetch2(data.frame(Name = "ExpoPatAnn", Key = "PPdMOCJzOZikWlKW"))
#Fetch2(data.frame(Name = "Exposure", Key = "YpqDCiPvtTUDUGMO"))
Fetch2(data.frame(Name = "PatPitPuhVed", Key = "hjUP8y5Rc2laF45F"))
PatPitPuhVed <- EvalOutput(PatPitPuhVed)
#print(xtable(VedKasTeh@output), type = "html")
#print(xtable(VedDesTeh@output), type = "html")
#print(xtable(PatPitPuhVed@output), type = "html")
#Exposure <- EvalOutput(Exposure, substitute = TRUE)
#Exposure
#print(xtable(Exposure@output), type = "html")
ExpoPatAnn <- EvalOutput(ExpoPatAnn, substitute = TRUE)
#print(xtable(ExpoPatAnn@output), type = "html")
#################################################################################
dose.response = ExpoPatAnn@output
Pathogen <- c("Kampylobakteeri","E.coli O157:H7","Rotavirus","Norovirus","Cryptosporidium","Giardia")
vaesto <- op_baseGetData("opasnet_base", "Op_fi2652")[,c("Ikä","Result")]
colnames(vaesto) <- c("Age", "Osuus")
vaesto$Populaatio <- vaesto$Osuus * Vaestonkoko
odotettu.elinika <- 81
colnames(dose.response)[colnames(dose.response) == "Patogeeni"] <- "Pathogen"
colnames(dose.response)[colnames(dose.response) == "ExpoPatAnnResult"] <- "P.inf"
colnames(dose.response)[colnames(dose.response) == "ExposureResult"] <- "Exp.pat"
#dose.response = data.frame(Pathogen = ExpoPatAnn@output[,"Patogeeni"],P.inf = ExpoPatAnn@output[,"ExpoPatAnnResult"])
P.ill.g.inf <- data.frame(Pathogen, P.ill.g.inf = c(0.33, 1 - (270 / 1540), 0.9, 0.7, 0.71, 1)) # todennäköisyys sairastua kun saa infektion
# Kampylobakteeri, DALYt per infektio
P.treat.g.ill.Kamp.Gastr <- data.frame(Pathogen = Pathogen[c(1,1,1)], Outcome = "Gastroenteritis", ill.treat = c("Untreated",
"General practitioner", "Hospitalised", "Unspecified")[c(1,2,3)], P.treat.g.ill = c(0.7627, 0.2373, 0.0097))
P.treat.ill.g.inf.Kamp.Gastr <- merge(P.treat.g.ill.Kamp.Gastr, P.ill.g.inf)
P.treat.ill.g.inf.Kamp.Gastr$P.treat.ill.g.inf <- P.treat.ill.g.inf.Kamp.Gastr$P.ill.g.inf *
P.treat.ill.g.inf.Kamp.Gastr$P.treat.g.ill
duration.ill.treat.Kamp.Gastr <- data.frame(Outcome = c("Gastroenteritis"), ill.treat = c("Untreated", "General practitioner",
"Hospitalised", "Unspecified")[c(1,2,3)], dur.ill = c(5.1 / 365, 8.4 / 365, 14.39 / 365))
severity.ill.treat.Kamp.Gastr <- data.frame(Outcome = c("Gastroenteritis"), ill.treat = c("Untreated", "General practitioner",
"Hospitalised", "Unspecified")[c(1,2,3)], sev.ill = c(0.067, 0.393, 0.393))
daly.ill.treat.Kamp.Gastr <- merge(P.treat.ill.g.inf.Kamp.Gastr, duration.ill.treat.Kamp.Gastr)
daly.ill.treat.Kamp.Gastr <- merge(daly.ill.treat.Kamp.Gastr, severity.ill.treat.Kamp.Gastr)
daly.ill.treat.Kamp.Gastr$dalys <- daly.ill.treat.Kamp.Gastr$P.treat.ill.g.inf * daly.ill.treat.Kamp.Gastr$dur.ill *
daly.ill.treat.Kamp.Gastr$sev.ill
P.death.g.ill.Gastr <- 0.0004
P.death.g.inf.Gastr <- P.death.g.ill.Gastr * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Kampylobakteeri"]
death.Gastr.life.lost <- 13.2
daly.death.Kamp.Gastr <- P.death.g.inf.Gastr * death.Gastr.life.lost
## GBS Kamp.
P.gbs.g.ill <- 2e-004
P.gbs.g.inf <- P.gbs.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Kampylobakteeri"]
dur.sev.factor.gbs <- data.frame(Outcome = c("Clinical GBS", "Residual GBS"), dur.sev.factor = c(0.29, 5.8)) # duration * severity * fraction?
daly.Kamp.gbs <- data.frame(dur.sev.factor.gbs$Outcome, dalys = dur.sev.factor.gbs$dur.sev.factor * P.gbs.g.inf)
P.death.g.gbs <- 0.08 / 3 # triangular 0.01, 0.02, 0.05
P.death.g.inf.gbs <- P.death.g.gbs * P.gbs.g.inf
death.gbs.life.lost <- 18.7
daly.death.Kamp.gbs <- P.death.g.inf.gbs * death.gbs.life.lost
## reactive arthritis Kamp.
P.arth.g.ill <- 0.02 # triangluar 0.01, 0.02, 0.03
P.arth.g.inf <- P.arth.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Kampylobakteeri"]
duration.arth <- 6 / 52
severity.arth <- 0.21
daly.Kamp.arth <- P.arth.g.inf * duration.arth * severity.arth
# E.coli
P.wd.g.ill <- 0.53 # watery diarrhea
P.wd.g.inf <- P.wd.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"]
severity.wd <- 0.067
duration.wd <- 3.4 / 365
daly.wd.Ecoli <- P.wd.g.inf * severity.wd * duration.wd
P.hc.g.ill <- 0.47
P.hc.g.inf <- P.hc.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"]
severity.hc <- 0.39
duration.hc <- 5.6 / 365
daly.hc.Ecoli <- P.hc.g.inf * severity.hc * duration.hc
P.death.g.ill.Ecoli <- 0.00027
P.death.g.inf.Ecoli <- P.death.g.ill.Ecoli * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"]
age.death.Ecoli <- 81 - 13.2
daly.death.Ecoli <- P.death.g.inf.Ecoli * (odotettu.elinika - age.death.Ecoli)
## Haemolytic uraemic syndrome (HUS)
P.hus.g.ill <- 0.01
P.hus.g.inf <- P.hus.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"]
severity.hus <- 0.93
duration.hus <- 21 / 365
daly.hus.Ecoli <- P.hus.g.inf * severity.hus * duration.hus
P.death.g.hus <- 0.04
P.death.hus.g.inf <- P.death.g.hus * P.hus.g.inf
age.death.hus.Ecoli <- 81 - 26.2
daly.death.hus.Ecoli <- P.death.hus.g.inf * (odotettu.elinika - age.death.hus.Ecoli)
## End Stage Renal Disease (ESRD)
P.esrd.g.hus <- 0.118
P.esrd.g.inf <- P.hus.g.inf * P.esrd.g.hus
severity.duration.hus <- 8.7 # severity * duration
daly.esrd.Ecoli <- P.esrd.g.inf * severity.duration.hus
P.death.g.esrd <- 0.0252
P.death.esrd.g.inf <- P.esrd.g.inf * P.death.g.esrd
age.death.esrd.Ecoli <- 81 - 34
daly.death.esrd.Ecoli <- P.death.esrd.g.inf * (odotettu.elinika - age.death.esrd.Ecoli)
# Rotavirus
P.treat.g.ill.Rotavirus <- data.frame(Pathogen = "Rotavirus", ill.treat = c("Untreated",
"General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.82,5),
rep(0.95, 10), rep(0.99, 50), rep(0.97, 17), rep(0.137, 5), rep(0.0244, 5), rep(0.0511, 5), rep(0.0127, 50),
rep(0.0299, 17), rep(0.0416, 5), rep(0.0213, 5), rep(0, 72)))
P.treat.ill.g.inf.Rotavirus <- merge(P.treat.g.ill.Rotavirus, P.ill.g.inf)
P.treat.ill.g.inf.Rotavirus$P.treat.g.inf <- P.treat.ill.g.inf.Rotavirus$P.ill.g.inf * P.treat.ill.g.inf.Rotavirus$P.treat.g.ill
duration.ill.treat.Rotavirus <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(4.9 / 365,
7.1 / 365, 7.7 / 365))
severity.ill.treat.Rotavirus <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067,
0.393, 0.393))
daly.ill.treat.Rotavirus <- merge(P.treat.ill.g.inf.Rotavirus, duration.ill.treat.Rotavirus)
daly.ill.treat.Rotavirus <- merge(daly.ill.treat.Rotavirus, severity.ill.treat.Rotavirus)
daly.ill.treat.Rotavirus$dalys <- daly.ill.treat.Rotavirus$P.treat.g.inf * daly.ill.treat.Rotavirus$dur.ill *
daly.ill.treat.Rotavirus$sev.ill
P.death.Rotavirus <- data.frame(Age = 0:81, P.death.g.ill = c(rep(2.13e-005, 5), rep(0, 77)))
P.death.Rotavirus$P.death.g.inf <- P.death.Rotavirus$P.death.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Rotavirus"]
P.death.Rotavirus$Life.lost <- odotettu.elinika - P.death.Rotavirus$Age
daly.death.Rotavirus <- data.frame(Age = P.death.Rotavirus$Age, dalys = P.death.Rotavirus$P.death.g.inf * P.death.Rotavirus$Life.lost)
# Norovirus
P.treat.g.ill.Norovirus <- data.frame(Pathogen = "Norovirus", ill.treat = c("Untreated",
"General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.94876706,5),
rep(0.9902, 5), rep(0.98239, 5), rep(0.98434, 51), rep(0.992741, 16), rep(0.0448,5), rep(8.6e-003, 5), rep(0.0154, 5),
rep(0.0137, 51), rep(6.17e-003, 16), rep(6.43e-003,5), rep(1.2e-003, 5), rep(2.21e-003, 5), rep(1.96e-003, 51),
rep(8.85e-004, 16)))
P.treat.ill.g.inf.Norovirus <- merge(P.treat.g.ill.Norovirus, P.ill.g.inf)
P.treat.ill.g.inf.Norovirus$P.treat.g.inf <- P.treat.ill.g.inf.Norovirus$P.ill.g.inf * P.treat.ill.g.inf.Norovirus$P.treat.g.ill
duration.ill.treat.Norovirus <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(3.8 / 365,
5.73 / 365, 7.23 / 365))
severity.ill.treat.Norovirus <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067,
0.393, 0.393))
daly.ill.treat.Norovirus <- merge(P.treat.ill.g.inf.Norovirus, duration.ill.treat.Norovirus)
daly.ill.treat.Norovirus <- merge(daly.ill.treat.Norovirus, severity.ill.treat.Norovirus)
daly.ill.treat.Norovirus$dalys <- daly.ill.treat.Norovirus$P.treat.g.inf * daly.ill.treat.Norovirus$dur.ill *
daly.ill.treat.Norovirus$sev.ill
P.death.Norovirus <- data.frame(Age = 0:81, P.death.g.ill = c(rep(2.94e-006, 5), rep(0, 61), rep(2.04e-004, 16)))
P.death.Norovirus$P.death.g.inf <- P.death.Norovirus$P.death.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Norovirus"]
P.death.Norovirus$Life.lost <- odotettu.elinika - P.death.Norovirus$Age
daly.death.Norovirus <- data.frame(Age = P.death.Norovirus$Age, dalys = P.death.Norovirus$P.death.g.inf * P.death.Norovirus$Life.lost)
# Cryptosporidium
P.treat.g.ill.Crypt <- data.frame(Pathogen = "Cryptosporidium", ill.treat = c("Untreated",
"General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.9175730049999999,5),
rep(0.80937, 5), rep(0.6810499999999999, 5), rep(0.9774191, 50), rep(0.94706, 17), rep(0.082,5), rep(0.188, 5), rep(0.316, 5),
rep(0.0209, 50), rep(0.0367, 17), rep(4.26e-004,5), rep(2.63e-003, 5), rep(2.95e-003, 5), rep(1.66e-003, 50), rep(0.0146, 17)))
P.treat.ill.g.inf.Crypt <- merge(P.treat.g.ill.Crypt, P.ill.g.inf)
P.treat.ill.g.inf.Crypt$P.treat.g.inf <- P.treat.ill.g.inf.Crypt$P.ill.g.inf * P.treat.ill.g.inf.Crypt$P.treat.g.ill
duration.ill.treat.Crypt <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(3.5 / 365,
7 /365, 18.4 / 365))
severity.ill.treat.Crypt <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067,
0.393, 0.393))
daly.ill.treat.Crypt <- merge(P.treat.ill.g.inf.Crypt, duration.ill.treat.Crypt)
daly.ill.treat.Crypt <- merge(daly.ill.treat.Crypt, severity.ill.treat.Crypt)
daly.ill.treat.Crypt$dalys <- daly.ill.treat.Crypt$P.treat.g.inf * daly.ill.treat.Crypt$dur.ill *
daly.ill.treat.Crypt$sev.ill
P.death.Crypt <- data.frame(Age = 0:81, P.death.g.ill = c(rep(9.95e-007, 5), rep(0, 10), rep(2.09e-005, 50), rep(1.64e-003, 17)))
P.death.Crypt$P.death.g.inf <- P.death.Crypt$P.death.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Cryptosporidium"]
P.death.Crypt$Life.lost <- odotettu.elinika - P.death.Crypt$Age
daly.death.Crypt <- data.frame(Age = P.death.Crypt$Age, dalys = P.death.Crypt$P.death.g.inf * P.death.Crypt$Life.lost)
# Giardia
P.treat.g.ill.Giardia <- data.frame(Pathogen = "Giardia", ill.treat = c("Untreated",
"General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.9376,5),
rep(0.91034, 5), rep(0.72642, 5), rep(0.92486, 50), 0.54596, rep(0.5365, 16), rep(0.0609,5), rep(0.0852, 5), rep(0.272, 5),
rep(0.0721, 50), rep(0.451, 17), rep(1.5e-003,5), rep(4.46e-003, 5), rep(1.58e-003, 5), rep(3.04e-003, 51), rep(0.0125, 16)))
P.treat.ill.g.inf.Giardia <- merge(P.treat.g.ill.Giardia, P.ill.g.inf)
P.treat.ill.g.inf.Giardia$P.treat.g.inf <- P.treat.ill.g.inf.Giardia$P.ill.g.inf * P.treat.ill.g.inf.Giardia$P.treat.g.ill
duration.ill.treat.Giardia <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(10 / 365,
10 /365, 30 / 365))
severity.ill.treat.Giardia <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067,
0.393, 0.393))
daly.ill.treat.Giardia <- merge(P.treat.ill.g.inf.Giardia, duration.ill.treat.Giardia)
daly.ill.treat.Giardia <- merge(daly.ill.treat.Giardia, severity.ill.treat.Giardia)
daly.ill.treat.Giardia$dalys <- daly.ill.treat.Giardia$P.treat.g.inf * daly.ill.treat.Giardia$dur.ill *
daly.ill.treat.Giardia$sev.ill
# yhteenveto DALYistä
Health.effects <- vaesto[,c("Age","Populaatio")]
Health.effects$Untreated.Gastr.Kamp <- daly.ill.treat.Kamp.Gastr[daly.ill.treat.Kamp.Gastr$ill.treat == "Untreated", c("dalys")]
Health.effects$GP.Gastr.Kamp <- daly.ill.treat.Kamp.Gastr[daly.ill.treat.Kamp.Gastr$ill.treat == "General practitioner", c("dalys")]
Health.effects$Hospitalised.Gastr.Kamp <- daly.ill.treat.Kamp.Gastr[daly.ill.treat.Kamp.Gastr$ill.treat == "Hospitalised", c("dalys")]
Health.effects$Death.Gastr.Kamp <- daly.death.Kamp.Gastr
Health.effects$Clinical.GBS.Kamp <- daly.Kamp.gbs$dalys[1]
Health.effects$Residual.GBS.Kamp <- daly.Kamp.gbs$dalys[2]
Health.effects$Death.GBS.Kamp <- daly.death.Kamp.gbs
Health.effects$Arth.Kamp <- daly.Kamp.arth
Health.effects$WD.Ecoli <- daly.wd.Ecoli
Health.effects$HC.Ecoli <- daly.hc.Ecoli
Health.effects$Death.Ecoli <- daly.death.Ecoli
Health.effects$HUS.Ecoli <- daly.hus.Ecoli
Health.effects$Death.HUS.Ecoli <- daly.death.hus.Ecoli
Health.effects$ESRD.Ecoli <- daly.esrd.Ecoli
Health.effects$Death.ESRD.Ecoli <- daly.death.esrd.Ecoli
Health.effects <- merge(Health.effects, daly.ill.treat.Rotavirus[daly.ill.treat.Rotavirus$ill.treat == "Untreated", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Rotavirus"
Health.effects <- merge(Health.effects, daly.ill.treat.Rotavirus[daly.ill.treat.Rotavirus$ill.treat == "General practitioner", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "GP.Rotavirus"
Health.effects <- merge(Health.effects, daly.ill.treat.Rotavirus[daly.ill.treat.Rotavirus$ill.treat == "Hospitalised", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Rotavirus"
Health.effects <- merge(Health.effects, daly.death.Rotavirus)
colnames(Health.effects)[ncol(Health.effects)] <- "Death.Rotavirus"
Health.effects <- merge(Health.effects, daly.ill.treat.Norovirus[daly.ill.treat.Norovirus$ill.treat == "Untreated", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Norovirus"
Health.effects <- merge(Health.effects, daly.ill.treat.Norovirus[daly.ill.treat.Norovirus$ill.treat == "General practitioner", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "GP.Norovirus"
Health.effects <- merge(Health.effects, daly.ill.treat.Norovirus[daly.ill.treat.Norovirus$ill.treat == "Hospitalised", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Norovirus"
Health.effects <- merge(Health.effects, daly.death.Norovirus)
colnames(Health.effects)[ncol(Health.effects)] <- "Death.Norovirus"
Health.effects <- merge(Health.effects, daly.ill.treat.Crypt[daly.ill.treat.Crypt$ill.treat == "Untreated", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Crypt"
Health.effects <- merge(Health.effects, daly.ill.treat.Crypt[daly.ill.treat.Crypt$ill.treat == "General practitioner", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "GP.Crypt"
Health.effects <- merge(Health.effects, daly.ill.treat.Crypt[daly.ill.treat.Crypt$ill.treat == "Hospitalised", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Crypt"
Health.effects <- merge(Health.effects, daly.death.Crypt)
colnames(Health.effects)[ncol(Health.effects)] <- "Death.Crypt"
Health.effects <- merge(Health.effects, daly.ill.treat.Giardia[daly.ill.treat.Giardia$ill.treat == "Untreated", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Giardia"
Health.effects <- merge(Health.effects, daly.ill.treat.Giardia[daly.ill.treat.Giardia$ill.treat == "General practitioner", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "GP.Giardia"
Health.effects <- merge(Health.effects, daly.ill.treat.Giardia[daly.ill.treat.Giardia$ill.treat == "Hospitalised", c("Age", "dalys")])
colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Giardia"
Health.effects <- reshape(Health.effects, idvar = c("Age"), times = colnames(Health.effects)[-c(1,2)], timevar = "Outcome",
varying = list(colnames(Health.effects)[-c(1,2)]), direction = "long")
colnames(Health.effects)[4] <- "P.daly.g.inf"
Health.effects$Pathogen <- NA
Health.effects$Pathogen[grep(".Kamp", Health.effects$Outcome)] <- Pathogen[1]
Health.effects$Pathogen[grep(".Ecoli", Health.effects$Outcome)] <- Pathogen[2]
Health.effects$Pathogen[grep(".Rotavirus", Health.effects$Outcome)] <- Pathogen[3]
Health.effects$Pathogen[grep(".Norovirus", Health.effects$Outcome)] <- Pathogen[4]
Health.effects$Pathogen[grep(".Crypt", Health.effects$Outcome)] <- Pathogen[5]
Health.effects$Pathogen[grep(".Giardia", Health.effects$Outcome)] <- Pathogen[6]
Health.effects <- merge(Health.effects, dose.response[,c("Pathogen", "P.inf")])
Health.effects$DALYs <- (1 - (1 - Health.effects$P.inf * Health.effects$P.daly.g.inf)^365) * Health.effects$Populaatio
temp <- merge(dose.response, P.ill.g.inf)
############# TULOKSET #########################################################################################################
cat("<span style='font-size: 1.2em;font-weight:bold;'>Patogeenien konsentraatio raakavedessä</span>\n")
print(xtable(RaaPatPitLuo@output), type='html') # Patogeenien konsentraatio raakavedessä
cat("<span style='font-size: 1.2em;font-weight:bold;'>Patogeenien log vähenemä puhdistuksessa</span>\n")
print(xtable(VedKasTeh@output), type='html') # Patogeenien log vähenemä puhdistuksessa
print(xtable(VedDesTeh@output), type='html') # Patogeenien log vähenemä desinfioinnissa
cat("<span style='font-size: 1.2em;font-weight:bold;'>Patogeeneille altistuminen ja infektion todennäköisyys</span>\n")
cat(colnames(dose.response), "\n")
#cat(colnames(ExpoPatAnn@output), "\n")
print(xtable(dose.response[,c("Pathogen", "Exp.pat", "P.inf", "VedDesTehSource")]), type="html") # Patogeeneille altistuminen ja infektion todennäköisyys
cat("<span style='font-size: 1.2em;font-weight:bold;'>Estimated health effects</span>\n")
cat(sum((1 - (1 - temp$P.ill.g.inf * temp$P.inf)^365) * Vaestonkoko, na.rm = TRUE), " stomach flus per year \n")
cat(sum(Health.effects$DALYs, na.rm = TRUE), " DALY's from stomach flus \n")
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