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.

+ Show code- Hide code 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.

Building minimum age: + Show code- Hide code 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.

Number of classes: 4 6 8 Type of classification: Equal Int Quantile Standard deviation Jenks + Show code- Hide code #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

• Ground water pathogen concentration

Ground water: Pathogenic concentrations Water resource: Ground water - Clean Ground water - Surface water stress Surface water - Low stress Surface water - Medium stress Surface water - High stress 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: Use values defined above Gotham City Kuopio 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: + Show code- Hide code 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 = "eJKTtosJiyZKwUs8"), 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")