Wikisym 2012 Demo

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.

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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.

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#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")

Health effects of Drinking Water Model

This example is model which is built for calculating health effects of drinking water and how water treatment processess affect to the outcome.

Ground water pathogen concentrations

Ground water: Pathogenic concentrations

Water resource:

Cambylobacter-concentration estimation (microbe/l):

E.coli O157:H7 -concentration estimation (microbe/l):

Rotavirus-concentration estimation (microbe/l):

Norovirus-concentration estimation (microbe/l):

Cryptosporidium-concentration estimation (microbe/l):

Giardia-concentration estimation (microbe/l):

City default values:

Water purification: Purification processess and chlorinesation

Available purification methods:
Traditional purification
Highly effective purification
Enhanced purification
Slow sand filtration
Limestone filtration
Activated carbon filtration
UV filtration
Ozonisation

Chlorine dose (mg/l):

Water network and consumers

Water consumption (ml):

Population:

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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)
    # fetching data from english Opasnet
    #Fetch2(data.frame(Name = "RaaPatPitLuo", Key = "kjRoRPqqAzhaG8qR"), evaluate = TRUE)
    temp <- tidy(op_baseGetData("opasnet_base", "Op_en5800"), objname = "RaaPatPitLuo")
    print(xtable(temp), type = "html")

    RaaPatPitLuo <- new("ovariable",
	name         = "RaaPatPitLuo",
	data         = temp
     )
    
   # RaaPatPitLuo@output <- RaaPatPitLuo@output[RaaPatPitLuo@output$Raakavesilähde == i.raw.class, ]
   RaaPatPitLuo@output <- RaaPatPitLuo@output[RaaPatPitLuo@output$Water_source == 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")

Wikisym 2012 Demo

Contents

Polygons on dynamic Google Maps

Points on dynamic Google Maps

Large quantity of points on a static Google Maps

Health effects of Drinking Water Model

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