formal/zimbabwe/PHIA/analysis.R
In jackolney/CascadeDashboard: A Shiny App for Investigating The Current Trajectory of HIV Care
# PEPFAR PHIA FILE
# Zimbabwe Cascade Tool Formal Analysis Script
rm(list=ls())
# AIM = Boil the calibration down to a set of REALLY NEAT FUNCTIONS.
setwd("~/git/CascadeDashboard/inst/app")
graphics.off()
quartz.options(w = 10, h = 8)
figFont <- "Avenir Next"
# Source initial files
source("../../formal/initial.R")
# GLOBAL
MasterName <- "Zimbabwe"
MasterData <- GetMasterDataSet(MasterName)
# write function that reduces incidence by some weight
# MasterData$incidence <- reduce_incidence(inc = MasterData$incidence, weight = 0.5)
# MAKE all PLHIV RED!
# MasterData$calib[MasterData$calib$indicator == "PLHIV", "weight"] <- "red"
# MAKE all RED!
# MasterData$calib[, "weight"] <- "red"
# ---- #
set.seed(100)
# ---- #
# MaxError <- 0.04
MaxError <- 0.05
MinNumber <- 100
# After first simulation, run this function
# by default, gives you 5% of simulations
# MaxError <- find_error_bound(runError, prop = 0.05)
# Define Parameter Range
# function can now be edited
# e.g. DefineParmRange(p = c(0, 1))
# parRange <- DefineParmRange()
# parRange <- DefineParmRange(p = c(0.86, 1), omega = c(0, 0.01))
# parRange for WHO-cascade-paper
# parRange <- DefineParmRange(p = c(0.7, 1), omega = c(0, 0.01))
# parRange <- DefineParmRange(
# p = c(0.7, 1),
# omega = c(0, 0.01),
# epsilon = c(100, 100),
# q = c(0.9, 1),
# kappa = c(0, 0.05)
# )
# the below works...
# parRange <- DefineParmRange(
# rho = c(0, 0.0005),
# q = c(0.9, 1),
# epsilon = c(100, 105),
# kappa = c(1, 2),
# gamma = c(2, 4),
# theta = c(0, 0.5),
# p = c(0.7, 0.9),
# omega = c(0, 0.01)
# )
# lets be more generous...
parRange <- DefineParmRange(
rho = c(0, 0.001),
q = c(0.9, 1),
epsilon = c(100, 100),
kappa = c(0, 2),
gamma = c(0, 4),
theta = c(0, 0.5),
p = c(0.7, 1),
omega = c(0, 0.01)
)
# Run Calibration
RunNSCalibration(
country = MasterName,
data = MasterData,
maxIterations = 1e4,
maxError = MaxError,
limit = MinNumber,
parRange = parRange,
targetIterations = 1e5)
graphics.off(); quartz.options(w = 8, h = 6)
BuildCD4CalibData_Thesis(year = 1, modelOut = modelOut)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/CD4-2010.pdf", type = "pdf")
graphics.off(); quartz.options(w = 8, h = 6)
BuildCD4CalibData_Thesis(year = 6, modelOut = modelOut)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/CD4-2015.pdf", type = "pdf")
graphics.off(); quartz.options(w = 9, h = 4)
BuildPHIAPlot(data = CalibOut)
# quartz.save(file = "~/Desktop/fig/PHIA.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/PHIA.pdf", type = "pdf")
################################################################################
#### PLOT ####
# Cascade in 2015
graphics.off(); quartz.options(w = 10, h = 4)
BuildCalibPlot_Thesis(data = CalibOut,
originalData = MasterData,
limit = MinNumber)
# quartz.save(file = "~/Desktop/fig/cascade.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/cascade-2015.pdf", type = "pdf")
# Error Histogram
graphics.off(); quartz.options(w = 6, h = 3)
BuildCalibrationHistogram_Thesis(
runError = runError,
maxError = MaxError)
# quartz.save(file = "~/Desktop/fig/hist.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/calib-hist.pdf", type = "pdf")
# Calibration Detail
graphics.off(); quartz.options(w = 10, h = 8)
BuildCalibDetailPlot_Thesis(
data = CalibOut,
originalData = MasterData,
limit = MinNumber)
# quartz.save(file = "~/Desktop/fig/detail.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/calib-detail.pdf", type = "pdf")
# Parameter Histograms
graphics.off(); quartz.options(w = 10, h = 4)
BuildCalibrationParameterHistGroup_Thesis()
# quartz.save(file = "~/Desktop/fig/par.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/par-hist.pdf", type = "pdf")
################################################################################
# DataReviewPlot
graphics.off(); quartz.options(w = 10, h = 4)
BuildDataReviewPlot_Thesis(data = MasterData$calib)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/calib-data.pdf", type = "pdf")
# save.image("../../formal/zimbabwe/PHIA/data.RData")
# Parameter means
round(colMeans(CalibParamOut), 4)
# Parameter values
a <- paste0(round(Quantile_95(CalibParamOut[["rho"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["rho"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["rho"]])[["upper"]], 4), "]")
b <- paste0(round(Quantile_95(CalibParamOut[["q"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["q"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["q"]])[["upper"]], 4), "]")
c <- paste0(round(Quantile_95(CalibParamOut[["epsilon"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["epsilon"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["epsilon"]])[["upper"]], 4), "]")
d <- paste0(round(Quantile_95(CalibParamOut[["kappa"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["kappa"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["kappa"]])[["upper"]], 4), "]")
e <- paste0(round(Quantile_95(CalibParamOut[["gamma"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["gamma"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["gamma"]])[["upper"]], 4), "]")
f <- paste0(round(Quantile_95(CalibParamOut[["theta"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["theta"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["theta"]])[["upper"]], 4), "]")
g <- paste0(round(Quantile_95(CalibParamOut[["p"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["p"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["p"]])[["upper"]], 4), "]")
h <- paste0(round(Quantile_95(CalibParamOut[["omega"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["omega"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["omega"]])[["upper"]], 4), "]")
list(a,b,c,d,e,f,g,h)
Quantile_95(CalibParamOut[["rho"]])
Quantile_95(CalibParamOut[["q"]])
Quantile_95(CalibParamOut[["epsilon"]])
Quantile_95(CalibParamOut[["kappa"]])
Quantile_95(CalibParamOut[["gamma"]])
Quantile_95(CalibParamOut[["theta"]])
Quantile_95(CalibParamOut[["p"]])
Quantile_95(CalibParamOut[["omega"]])
# Mean of over-riding parameter bounds from non-shiny interface
################################################################################
# Projection
AdvCalib <- data.frame(NatMort = 0.005, HIVMort = 1)
# Single Powers Plot
graphics.off(); quartz.options(w = 8, h = 4)
GenSinglePowersPlot_Thesis()
quartz.save(file = "~/Desktop/fig/powers.pdf", type = "pdf")
# CareCascade Plot
graphics.off(); quartz.options(w = 10, h = 4)
GenCascadePlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/cascade-projection.pdf", type = "pdf")
# 90-90-90 Plot
graphics.off(); quartz.options(w = 9, h = 4)
Gen909090Plot_Thesis()
# quartz.save(file = "~/Desktop/fig/90-90-90.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/90-90-90.pdf", type = "pdf")
# Powers Plot
graphics.off(); quartz.options(w = 15, h = 4)
GenPowersCascadePlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/cascade-powers.pdf", type = "pdf")
# New Infections
graphics.off(); quartz.options(w = 6, h = 4)
GenNewInfPlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/new-infections.pdf", type = "pdf")
# AIDS Deaths
graphics.off(); quartz.options(w = 6, h = 4)
GenAidsDeathsPlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/AIDS-deaths.pdf", type = "pdf")
# Discrete Cascade
graphics.off(); quartz.options(w = 10, h = 4)
GenDiscreteCascade_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/cascade-discrete.pdf", type = "pdf")
# CD4 distribution at ART initiation
graphics.off(); quartz.options(w = 8, h = 6)
BuildCD4Data_Thesis(year = 251)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/CD4-2020.pdf", type = "pdf")
# NUMBERS
t0 <- GetCascadeData(1) # t0 = 1
t5 <- GetCascadeData(251) # t5 = (5 / 0.02) + 1 [t0]
# PLHIV Estimate in 2015
scales::comma(round(t0$res[1], -3))
1.385e+6
scales::comma(round(t5$res[1], -3))
1.534e+6
1.534e+6 / 1.385e+6
# DIAGNOSED
scales::comma(round(t0$res[2], -3))
1.242e+6
scales::comma(round(t5$res[2], -3))
1.406e+6
1.406e+6 / 1.242e+6
# On ART
scales::comma(round(t0$res[4], -3))
scales::comma(round(t5$res[4], -3))
round(t5$res[4], -3) / round(t0$res[4], -3)
30\% (0.876M to 1.140M)
# Viral suppression
scales::comma(round(t0$res[5], -3))
scales::comma(round(t5$res[5], -3))
round(t5$res[5], -3) / round(t0$res[5], -3)
33\% (0.773M to 1.027M)
23 - 14
t0
t0$res[5]/t0$res[1]
t5$res[5]/t5$res[1]
# save.image("../../formal/zimbabwe/PHIA/data.RData")
# load("../../formal/zimbabwe/PHIA/data.RData")
################################################################################
# Optimisation
intSwitch <- data.frame(
testing = TRUE,
linkage = TRUE,
preRetention = TRUE,
initiation = TRUE,
adherence = TRUE,
retention = TRUE
)
# These need updating
# and re-write this definition, it looks vile
# OptInput <- c()
# OptInput$intValue_rho <- parRange["rho", "max"]
# OptInput$intValue_q <- parRange["q", "max"]
# OptInput$intValue_kappa <- parRange["kappa", "min"]
# OptInput$intValue_gamma <- parRange["gamma", "max"]
# OptInput$intValue_sigma <- 0.5
# OptInput$intValue_omega <- parRange["omega", "min"]
OptInput <- c()
OptInput$intValue_rho <- 1
OptInput$intValue_q <- 1
OptInput$intValue_kappa <- 0
OptInput$intValue_gamma <- 4
OptInput$intValue_sigma <- 0.5
OptInput$intValue_omega <- 0
reactiveCost <- data.frame(
test = 10,
link = 40,
care = 40,
art = 367
)
# This is used by the function 'AdjustHIVTetsCost'
SafeReactiveCost <- data.frame(
test = 10,
link = 40,
care = 40,
art = 367
)
custom <- data.frame(target = 0.9^3)
AdvCalib <- data.frame(NatMort = 0.005, HIVMort = 1)
reactiveAdjustCost <- data.frame(switch = TRUE)
intLength = 2
AdjustHIVTestCost()
theOut <- RunNSOptimisation(propRuns = 0.1, intLength = intLength)
# Frontier Plot (optResults comes from RunNSOptimisation
graphics.off(); quartz.options(w = 8, h = 4)
BuildFrontierPlot_Thesis(CalibParamOut = CalibParamOut, optResults = optResults, target = 0.9^3)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/opt/frontier.pdf", type = "pdf")
# FIGURE GENERATION
graphics.off(); quartz.options(w = 8, h = 4)
BuildChangesPlot_Thesis(CalibParamOut = CalibParamOut, optResults = optResults, target = 0.9^3)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/opt/changes.pdf", type = "pdf")
################################################################################
# TABULATE RESULTS #
simLength <- dim(GetParaMatrixRun(cParamOut = CalibParamOut, runNumber = 1, length = intLength))[1]
optRuns <- WhichAchieved73(simData = theOut, simLength = simLength, target = custom$target)
optRuns
frontierList <- GetFrontiers(simData = theOut, optRuns = optRuns, simLength = simLength)
frontierList
intRes <- RunInterpolation(simData = theOut, optRuns = optRuns, simLength = simLength, frontierList = frontierList, target = custom$target)
colMeans(intRes)
results <- intRes[,c("iTest","iLink","iPreR","iInit","iAdhr","iRetn")]
results$iPreR <- abs(results$iPreR)
results$iRetn <- abs(results$iRetn)
results[results$iTest < 0, "iTest"] <- 0
results[results$iLink < 0, "iLink"] <- 0
results[results$iInit < 0, "iInit"] <- 0
results[results$iAdhr < 0, "iAdhr"] <- 0
results$run <- 1:dim(results)[1]
resTable <- results
resTable[resTable$iTest < 0, "iTest"] <- 0
resTable[resTable$iLink < 0, "iLink"] <- 0
resTable[resTable$iInit < 0, "iInit"] <- 0
resTable[resTable$iAdhr < 0, "iAdhr"] <- 0
colMeans(resTable)
# BASELINE
b1 <- paste0("iCost = ", scales::dollar(Quantile_95(BaselineCost)["mean"] / 5), " [", scales::dollar(Quantile_95(BaselineCost)["lower"] / 5), " to ", scales::dollar(Quantile_95(BaselineCost)["upper"] / 5), "]")
b2 <- paste0("iTest = ", scales::comma(round(Quantile_95(BaselineTest)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineTest)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineTest)["upper"] / 5, 0)), "]")
b3 <- paste0("iLink = ", scales::comma(round(Quantile_95(BaselineLink)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineLink)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineLink)["upper"] / 5, 0)), "]")
b4 <- paste0("iPreR = ", scales::comma(round(Quantile_95(BaselinePreR)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselinePreR)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselinePreR)["upper"] / 5, 0)), "]")
b5 <- paste0("iInit = ", scales::comma(round(Quantile_95(BaselineInit)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineInit)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineInit)["upper"] / 5, 0)), "]")
b6 <- paste0("iAdhr = ", scales::comma(round(Quantile_95(BaselineAdhr)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineAdhr)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineAdhr)["upper"] / 5, 0)), "]")
b7 <- paste0("iRetn = ", scales::comma(round(Quantile_95(BaselineRetn)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineRetn)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineRetn)["upper"] / 5, 0)), "]")
b1
b2
b3
b4
b5
b6
b7
# INTERVENTIONS
round(Quantile_95(intRes[,"iCost"])["mean"] / 1e6, 2)
i1 <- paste0("iCost = ", scales::dollar(Quantile_95(intRes[,"iCost"])["mean"] / 5), " [", scales::dollar(Quantile_95(intRes[,"iCost"])["lower"] / 5), " to ", scales::dollar(Quantile_95(intRes[,"iCost"])["upper"] / 5), "]")
i2 <- paste0("iTest = ", scales::comma(round(Quantile_95(resTable[,"iTest"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iTest"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iTest"])["upper"] / 5, 0)), "]")
i3 <- paste0("iLink = ", scales::comma(round(Quantile_95(resTable[,"iLink"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iLink"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iLink"])["upper"] / 5, 0)), "]")
i4 <- paste0("iPreR = ", scales::comma(round(Quantile_95(resTable[,"iPreR"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iPreR"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iPreR"])["upper"] / 5, 0)), "]")
i5 <- paste0("iInit = ", scales::comma(round(Quantile_95(resTable[,"iInit"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iInit"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iInit"])["upper"] / 5, 0)), "]")
i6 <- paste0("iAdhr = ", scales::comma(round(Quantile_95(resTable[,"iAdhr"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iAdhr"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iAdhr"])["upper"] / 5, 0)), "]")
i7 <- paste0("iRetn = ", scales::comma(round(Quantile_95(resTable[,"iRetn"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iRetn"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iRetn"])["upper"] / 5, 0)), "]")
i1
i2
i3
i4
i5
i6
i7
Quantile_95(resTable[,"iTest"]) / 5
Quantile_95(resTable[,"iLink"]) / 5
Quantile_95(resTable[,"iPreR"]) / 5
Quantile_95(resTable[,"iInit"]) / 5
Quantile_95(resTable[,"iAdhr"]) / 5
Quantile_95(resTable[,"iRetn"]) / 5
Quantile_95(intRes[,"iTCst"]) / 5
################################################################################
# SAVE IMAGE #
# save.image("../../formal/zimbabwe/PHIA/data.RData")
# save.image("../../formal/zimbabwe/PHIA/big-data.RData")
################################################################################
# PEPFAR PHIA 2016 Analysis
Get909090Data_2016 <- function(yr) {
result <- GetModel()
# Always aiming for 2020 here (5.02 / 0.02)
# year <- 251
year <- ((yr - 2015) / 0.02)
NX_data <- unlist(lapply(result, function(x) sum(x$N[year])))
DX_data <- unlist(lapply(result, function(x) sum(x$Dx[year], x$Care[year], x$PreLtfu[year], x$ART[year], x$Ltfu[year])))
TX_data <- unlist(lapply(result, function(x) sum(x$ART[year])))
VS_data <- unlist(lapply(result, function(x) sum(x$Vs[year])))
UN_90 <- Quantile_95(DX_data / NX_data)
UN_9090 <- Quantile_95(TX_data / DX_data)
UN_909090 <- Quantile_95(VS_data / TX_data)
res <- c(UN_90[["mean"]], UN_9090[["mean"]], UN_909090[["mean"]])
min <- c(UN_90[["lower"]], UN_9090[["lower"]], UN_909090[["lower"]])
max <- c(UN_90[["upper"]], UN_9090[["upper"]], UN_909090[["upper"]])
def <- c("Diagnosed / PLHIV", "On Treatment / Diagnosed", "Virally Suppressed / On Treatment")
out <- data.frame(def, res, min, max)
out$def <- factor(out$def, levels = c("Diagnosed / PLHIV", "On Treatment / Diagnosed", "Virally Suppressed / On Treatment"))
out
}
test <- Get909090Data_2016(yr = 2016)
Gen909090Plot_2016 <- function(yr) {
out <- Get909090Data_2016(yr = yr)
cfill <- rev(brewer.pal(9,"Blues")[6:8])
vbOut1 <- round(out[out$def == "Diagnosed / PLHIV", "res"] * 100, digits = 0)
vbOut2 <- round(out[out$def == "On Treatment / Diagnosed", "res"] * 100, digits = 0)
vbOut3 <- round(out[out$def == "Virally Suppressed / On Treatment", "res"] * 100, digits = 0)
ggOut <- ggplot(out, aes(x = def, y = res))
ggOut <- ggOut + geom_bar(aes(fill = def), position = 'dodge', stat = 'identity')
ggOut <- ggOut + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max), width = 0.2, size = 0.5)
ggOut <- ggOut + scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1), labels = scales::percent, expand = c(0, 0))
ggOut <- ggOut + scale_fill_manual(values = cfill)
ggOut <- ggOut + geom_abline(intercept = 0.9, slope = 0)
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + theme(plot.title = element_text(hjust = 0.5))
ggOut <- ggOut + theme(title = element_text(size = 20))
ggOut <- ggOut + theme(axis.title = element_blank())
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
ggOut <- ggOut + theme(axis.text.x = element_text(size = 12))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 12))
ggOut <- ggOut + theme(legend.position = "none")
ggOut <- ggOut + theme(plot.background = element_blank())
ggOut <- ggOut + theme(panel.background = element_blank())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + geom_label(aes(x = def, label = scales::percent(round(out$res, digits = 2))), size = 4)
ggOut
}
Gen909090Plot_2016(yr = 2016)
###
head(CalibOut)
test <- CalibOut
plhiv_2010 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2010, "value"])
plhiv_2011 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2011, "value"])
plhiv_2012 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2012, "value"])
plhiv_2013 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2013, "value"])
plhiv_2014 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2014, "value"])
plhiv_2015 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2015, "value"])
plhiv <- c(plhiv_2010, plhiv_2011, plhiv_2012, plhiv_2013, plhiv_2014, plhiv_2015)
inc <- as.double(MasterData$incidence[2,3:8])
inc
rate <- inc / plhiv
year <- seq(2010, 2015, 1)
df <- data.frame(year, rate)
graphics.off(); quartz.options(w = 5, h = 3)
ggplot(df, aes(x = year, y = rate)) +
geom_line() +
ggtitle("Transmission Rate", subtitle = "New Infections / Total PLHIV")
quartz.save(file = "~/Desktop/fig/transmission-rate.pdf", type = "pdf")
jackolney/CascadeDashboard documentation built on May 18, 2019, 7:56 a.m.
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# PEPFAR PHIA FILE
# Zimbabwe Cascade Tool Formal Analysis Script
rm(list=ls())
# AIM = Boil the calibration down to a set of REALLY NEAT FUNCTIONS.
setwd("~/git/CascadeDashboard/inst/app")
graphics.off()
quartz.options(w = 10, h = 8)
figFont <- "Avenir Next"
# Source initial files
source("../../formal/initial.R")
# GLOBAL
MasterName <- "Zimbabwe"
MasterData <- GetMasterDataSet(MasterName)
# write function that reduces incidence by some weight
# MasterData$incidence <- reduce_incidence(inc = MasterData$incidence, weight = 0.5)
# MAKE all PLHIV RED!
# MasterData$calib[MasterData$calib$indicator == "PLHIV", "weight"] <- "red"
# MAKE all RED!
# MasterData$calib[, "weight"] <- "red"
# ---- #
set.seed(100)
# ---- #
# MaxError <- 0.04
MaxError <- 0.05
MinNumber <- 100
# After first simulation, run this function
# by default, gives you 5% of simulations
# MaxError <- find_error_bound(runError, prop = 0.05)
# Define Parameter Range
# function can now be edited
# e.g. DefineParmRange(p = c(0, 1))
# parRange <- DefineParmRange()
# parRange <- DefineParmRange(p = c(0.86, 1), omega = c(0, 0.01))
# parRange for WHO-cascade-paper
# parRange <- DefineParmRange(p = c(0.7, 1), omega = c(0, 0.01))
# parRange <- DefineParmRange(
# p = c(0.7, 1),
# omega = c(0, 0.01),
# epsilon = c(100, 100),
# q = c(0.9, 1),
# kappa = c(0, 0.05)
# )
# the below works...
# parRange <- DefineParmRange(
# rho = c(0, 0.0005),
# q = c(0.9, 1),
# epsilon = c(100, 105),
# kappa = c(1, 2),
# gamma = c(2, 4),
# theta = c(0, 0.5),
# p = c(0.7, 0.9),
# omega = c(0, 0.01)
# )
# lets be more generous...
parRange <- DefineParmRange(
rho = c(0, 0.001),
q = c(0.9, 1),
epsilon = c(100, 100),
kappa = c(0, 2),
gamma = c(0, 4),
theta = c(0, 0.5),
p = c(0.7, 1),
omega = c(0, 0.01)
)
# Run Calibration
RunNSCalibration(
country = MasterName,
data = MasterData,
maxIterations = 1e4,
maxError = MaxError,
limit = MinNumber,
parRange = parRange,
targetIterations = 1e5)
graphics.off(); quartz.options(w = 8, h = 6)
BuildCD4CalibData_Thesis(year = 1, modelOut = modelOut)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/CD4-2010.pdf", type = "pdf")
graphics.off(); quartz.options(w = 8, h = 6)
BuildCD4CalibData_Thesis(year = 6, modelOut = modelOut)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/CD4-2015.pdf", type = "pdf")
graphics.off(); quartz.options(w = 9, h = 4)
BuildPHIAPlot(data = CalibOut)
# quartz.save(file = "~/Desktop/fig/PHIA.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/PHIA.pdf", type = "pdf")
################################################################################
#### PLOT ####
# Cascade in 2015
graphics.off(); quartz.options(w = 10, h = 4)
BuildCalibPlot_Thesis(data = CalibOut,
originalData = MasterData,
limit = MinNumber)
# quartz.save(file = "~/Desktop/fig/cascade.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/cascade-2015.pdf", type = "pdf")
# Error Histogram
graphics.off(); quartz.options(w = 6, h = 3)
BuildCalibrationHistogram_Thesis(
runError = runError,
maxError = MaxError)
# quartz.save(file = "~/Desktop/fig/hist.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/calib-hist.pdf", type = "pdf")
# Calibration Detail
graphics.off(); quartz.options(w = 10, h = 8)
BuildCalibDetailPlot_Thesis(
data = CalibOut,
originalData = MasterData,
limit = MinNumber)
# quartz.save(file = "~/Desktop/fig/detail.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/calib-detail.pdf", type = "pdf")
# Parameter Histograms
graphics.off(); quartz.options(w = 10, h = 4)
BuildCalibrationParameterHistGroup_Thesis()
# quartz.save(file = "~/Desktop/fig/par.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/par-hist.pdf", type = "pdf")
################################################################################
# DataReviewPlot
graphics.off(); quartz.options(w = 10, h = 4)
BuildDataReviewPlot_Thesis(data = MasterData$calib)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/cal/calib-data.pdf", type = "pdf")
# save.image("../../formal/zimbabwe/PHIA/data.RData")
# Parameter means
round(colMeans(CalibParamOut), 4)
# Parameter values
a <- paste0(round(Quantile_95(CalibParamOut[["rho"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["rho"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["rho"]])[["upper"]], 4), "]")
b <- paste0(round(Quantile_95(CalibParamOut[["q"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["q"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["q"]])[["upper"]], 4), "]")
c <- paste0(round(Quantile_95(CalibParamOut[["epsilon"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["epsilon"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["epsilon"]])[["upper"]], 4), "]")
d <- paste0(round(Quantile_95(CalibParamOut[["kappa"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["kappa"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["kappa"]])[["upper"]], 4), "]")
e <- paste0(round(Quantile_95(CalibParamOut[["gamma"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["gamma"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["gamma"]])[["upper"]], 4), "]")
f <- paste0(round(Quantile_95(CalibParamOut[["theta"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["theta"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["theta"]])[["upper"]], 4), "]")
g <- paste0(round(Quantile_95(CalibParamOut[["p"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["p"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["p"]])[["upper"]], 4), "]")
h <- paste0(round(Quantile_95(CalibParamOut[["omega"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["omega"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["omega"]])[["upper"]], 4), "]")
list(a,b,c,d,e,f,g,h)
Quantile_95(CalibParamOut[["rho"]])
Quantile_95(CalibParamOut[["q"]])
Quantile_95(CalibParamOut[["epsilon"]])
Quantile_95(CalibParamOut[["kappa"]])
Quantile_95(CalibParamOut[["gamma"]])
Quantile_95(CalibParamOut[["theta"]])
Quantile_95(CalibParamOut[["p"]])
Quantile_95(CalibParamOut[["omega"]])
# Mean of over-riding parameter bounds from non-shiny interface
################################################################################
# Projection
AdvCalib <- data.frame(NatMort = 0.005, HIVMort = 1)
# Single Powers Plot
graphics.off(); quartz.options(w = 8, h = 4)
GenSinglePowersPlot_Thesis()
quartz.save(file = "~/Desktop/fig/powers.pdf", type = "pdf")
# CareCascade Plot
graphics.off(); quartz.options(w = 10, h = 4)
GenCascadePlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/cascade-projection.pdf", type = "pdf")
# 90-90-90 Plot
graphics.off(); quartz.options(w = 9, h = 4)
Gen909090Plot_Thesis()
# quartz.save(file = "~/Desktop/fig/90-90-90.pdf", type = "pdf")
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/90-90-90.pdf", type = "pdf")
# Powers Plot
graphics.off(); quartz.options(w = 15, h = 4)
GenPowersCascadePlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/cascade-powers.pdf", type = "pdf")
# New Infections
graphics.off(); quartz.options(w = 6, h = 4)
GenNewInfPlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/new-infections.pdf", type = "pdf")
# AIDS Deaths
graphics.off(); quartz.options(w = 6, h = 4)
GenAidsDeathsPlot_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/AIDS-deaths.pdf", type = "pdf")
# Discrete Cascade
graphics.off(); quartz.options(w = 10, h = 4)
GenDiscreteCascade_Thesis()
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/cascade-discrete.pdf", type = "pdf")
# CD4 distribution at ART initiation
graphics.off(); quartz.options(w = 8, h = 6)
BuildCD4Data_Thesis(year = 251)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/pro/CD4-2020.pdf", type = "pdf")
# NUMBERS
t0 <- GetCascadeData(1) # t0 = 1
t5 <- GetCascadeData(251) # t5 = (5 / 0.02) + 1 [t0]
# PLHIV Estimate in 2015
scales::comma(round(t0$res[1], -3))
1.385e+6
scales::comma(round(t5$res[1], -3))
1.534e+6
1.534e+6 / 1.385e+6
# DIAGNOSED
scales::comma(round(t0$res[2], -3))
1.242e+6
scales::comma(round(t5$res[2], -3))
1.406e+6
1.406e+6 / 1.242e+6
# On ART
scales::comma(round(t0$res[4], -3))
scales::comma(round(t5$res[4], -3))
round(t5$res[4], -3) / round(t0$res[4], -3)
30\% (0.876M to 1.140M)
# Viral suppression
scales::comma(round(t0$res[5], -3))
scales::comma(round(t5$res[5], -3))
round(t5$res[5], -3) / round(t0$res[5], -3)
33\% (0.773M to 1.027M)
23 - 14
t0
t0$res[5]/t0$res[1]
t5$res[5]/t5$res[1]
# save.image("../../formal/zimbabwe/PHIA/data.RData")
# load("../../formal/zimbabwe/PHIA/data.RData")
################################################################################
# Optimisation
intSwitch <- data.frame(
testing = TRUE,
linkage = TRUE,
preRetention = TRUE,
initiation = TRUE,
adherence = TRUE,
retention = TRUE
)
# These need updating
# and re-write this definition, it looks vile
# OptInput <- c()
# OptInput$intValue_rho <- parRange["rho", "max"]
# OptInput$intValue_q <- parRange["q", "max"]
# OptInput$intValue_kappa <- parRange["kappa", "min"]
# OptInput$intValue_gamma <- parRange["gamma", "max"]
# OptInput$intValue_sigma <- 0.5
# OptInput$intValue_omega <- parRange["omega", "min"]
OptInput <- c()
OptInput$intValue_rho <- 1
OptInput$intValue_q <- 1
OptInput$intValue_kappa <- 0
OptInput$intValue_gamma <- 4
OptInput$intValue_sigma <- 0.5
OptInput$intValue_omega <- 0
reactiveCost <- data.frame(
test = 10,
link = 40,
care = 40,
art = 367
)
# This is used by the function 'AdjustHIVTetsCost'
SafeReactiveCost <- data.frame(
test = 10,
link = 40,
care = 40,
art = 367
)
custom <- data.frame(target = 0.9^3)
AdvCalib <- data.frame(NatMort = 0.005, HIVMort = 1)
reactiveAdjustCost <- data.frame(switch = TRUE)
intLength = 2
AdjustHIVTestCost()
theOut <- RunNSOptimisation(propRuns = 0.1, intLength = intLength)
# Frontier Plot (optResults comes from RunNSOptimisation
graphics.off(); quartz.options(w = 8, h = 4)
BuildFrontierPlot_Thesis(CalibParamOut = CalibParamOut, optResults = optResults, target = 0.9^3)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/opt/frontier.pdf", type = "pdf")
# FIGURE GENERATION
graphics.off(); quartz.options(w = 8, h = 4)
BuildChangesPlot_Thesis(CalibParamOut = CalibParamOut, optResults = optResults, target = 0.9^3)
quartz.save(file = "../../formal/zimbabwe/PHIA/fig/opt/changes.pdf", type = "pdf")
################################################################################
# TABULATE RESULTS #
simLength <- dim(GetParaMatrixRun(cParamOut = CalibParamOut, runNumber = 1, length = intLength))[1]
optRuns <- WhichAchieved73(simData = theOut, simLength = simLength, target = custom$target)
optRuns
frontierList <- GetFrontiers(simData = theOut, optRuns = optRuns, simLength = simLength)
frontierList
intRes <- RunInterpolation(simData = theOut, optRuns = optRuns, simLength = simLength, frontierList = frontierList, target = custom$target)
colMeans(intRes)
results <- intRes[,c("iTest","iLink","iPreR","iInit","iAdhr","iRetn")]
results$iPreR <- abs(results$iPreR)
results$iRetn <- abs(results$iRetn)
results[results$iTest < 0, "iTest"] <- 0
results[results$iLink < 0, "iLink"] <- 0
results[results$iInit < 0, "iInit"] <- 0
results[results$iAdhr < 0, "iAdhr"] <- 0
results$run <- 1:dim(results)[1]
resTable <- results
resTable[resTable$iTest < 0, "iTest"] <- 0
resTable[resTable$iLink < 0, "iLink"] <- 0
resTable[resTable$iInit < 0, "iInit"] <- 0
resTable[resTable$iAdhr < 0, "iAdhr"] <- 0
colMeans(resTable)
# BASELINE
b1 <- paste0("iCost = ", scales::dollar(Quantile_95(BaselineCost)["mean"] / 5), " [", scales::dollar(Quantile_95(BaselineCost)["lower"] / 5), " to ", scales::dollar(Quantile_95(BaselineCost)["upper"] / 5), "]")
b2 <- paste0("iTest = ", scales::comma(round(Quantile_95(BaselineTest)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineTest)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineTest)["upper"] / 5, 0)), "]")
b3 <- paste0("iLink = ", scales::comma(round(Quantile_95(BaselineLink)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineLink)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineLink)["upper"] / 5, 0)), "]")
b4 <- paste0("iPreR = ", scales::comma(round(Quantile_95(BaselinePreR)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselinePreR)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselinePreR)["upper"] / 5, 0)), "]")
b5 <- paste0("iInit = ", scales::comma(round(Quantile_95(BaselineInit)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineInit)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineInit)["upper"] / 5, 0)), "]")
b6 <- paste0("iAdhr = ", scales::comma(round(Quantile_95(BaselineAdhr)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineAdhr)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineAdhr)["upper"] / 5, 0)), "]")
b7 <- paste0("iRetn = ", scales::comma(round(Quantile_95(BaselineRetn)["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(BaselineRetn)["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(BaselineRetn)["upper"] / 5, 0)), "]")
b1
b2
b3
b4
b5
b6
b7
# INTERVENTIONS
round(Quantile_95(intRes[,"iCost"])["mean"] / 1e6, 2)
i1 <- paste0("iCost = ", scales::dollar(Quantile_95(intRes[,"iCost"])["mean"] / 5), " [", scales::dollar(Quantile_95(intRes[,"iCost"])["lower"] / 5), " to ", scales::dollar(Quantile_95(intRes[,"iCost"])["upper"] / 5), "]")
i2 <- paste0("iTest = ", scales::comma(round(Quantile_95(resTable[,"iTest"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iTest"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iTest"])["upper"] / 5, 0)), "]")
i3 <- paste0("iLink = ", scales::comma(round(Quantile_95(resTable[,"iLink"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iLink"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iLink"])["upper"] / 5, 0)), "]")
i4 <- paste0("iPreR = ", scales::comma(round(Quantile_95(resTable[,"iPreR"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iPreR"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iPreR"])["upper"] / 5, 0)), "]")
i5 <- paste0("iInit = ", scales::comma(round(Quantile_95(resTable[,"iInit"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iInit"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iInit"])["upper"] / 5, 0)), "]")
i6 <- paste0("iAdhr = ", scales::comma(round(Quantile_95(resTable[,"iAdhr"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iAdhr"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iAdhr"])["upper"] / 5, 0)), "]")
i7 <- paste0("iRetn = ", scales::comma(round(Quantile_95(resTable[,"iRetn"])["mean"] / 5, 0)), " [", scales::comma(round(Quantile_95(resTable[,"iRetn"])["lower"] / 5, 0)), " to ", scales::comma(round(Quantile_95(resTable[,"iRetn"])["upper"] / 5, 0)), "]")
i1
i2
i3
i4
i5
i6
i7
Quantile_95(resTable[,"iTest"]) / 5
Quantile_95(resTable[,"iLink"]) / 5
Quantile_95(resTable[,"iPreR"]) / 5
Quantile_95(resTable[,"iInit"]) / 5
Quantile_95(resTable[,"iAdhr"]) / 5
Quantile_95(resTable[,"iRetn"]) / 5
Quantile_95(intRes[,"iTCst"]) / 5
################################################################################
# SAVE IMAGE #
# save.image("../../formal/zimbabwe/PHIA/data.RData")
# save.image("../../formal/zimbabwe/PHIA/big-data.RData")
################################################################################
# PEPFAR PHIA 2016 Analysis
Get909090Data_2016 <- function(yr) {
result <- GetModel()
# Always aiming for 2020 here (5.02 / 0.02)
# year <- 251
year <- ((yr - 2015) / 0.02)
NX_data <- unlist(lapply(result, function(x) sum(x$N[year])))
DX_data <- unlist(lapply(result, function(x) sum(x$Dx[year], x$Care[year], x$PreLtfu[year], x$ART[year], x$Ltfu[year])))
TX_data <- unlist(lapply(result, function(x) sum(x$ART[year])))
VS_data <- unlist(lapply(result, function(x) sum(x$Vs[year])))
UN_90 <- Quantile_95(DX_data / NX_data)
UN_9090 <- Quantile_95(TX_data / DX_data)
UN_909090 <- Quantile_95(VS_data / TX_data)
res <- c(UN_90[["mean"]], UN_9090[["mean"]], UN_909090[["mean"]])
min <- c(UN_90[["lower"]], UN_9090[["lower"]], UN_909090[["lower"]])
max <- c(UN_90[["upper"]], UN_9090[["upper"]], UN_909090[["upper"]])
def <- c("Diagnosed / PLHIV", "On Treatment / Diagnosed", "Virally Suppressed / On Treatment")
out <- data.frame(def, res, min, max)
out$def <- factor(out$def, levels = c("Diagnosed / PLHIV", "On Treatment / Diagnosed", "Virally Suppressed / On Treatment"))
out
}
test <- Get909090Data_2016(yr = 2016)
Gen909090Plot_2016 <- function(yr) {
out <- Get909090Data_2016(yr = yr)
cfill <- rev(brewer.pal(9,"Blues")[6:8])
vbOut1 <- round(out[out$def == "Diagnosed / PLHIV", "res"] * 100, digits = 0)
vbOut2 <- round(out[out$def == "On Treatment / Diagnosed", "res"] * 100, digits = 0)
vbOut3 <- round(out[out$def == "Virally Suppressed / On Treatment", "res"] * 100, digits = 0)
ggOut <- ggplot(out, aes(x = def, y = res))
ggOut <- ggOut + geom_bar(aes(fill = def), position = 'dodge', stat = 'identity')
ggOut <- ggOut + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max), width = 0.2, size = 0.5)
ggOut <- ggOut + scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1), labels = scales::percent, expand = c(0, 0))
ggOut <- ggOut + scale_fill_manual(values = cfill)
ggOut <- ggOut + geom_abline(intercept = 0.9, slope = 0)
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + theme(plot.title = element_text(hjust = 0.5))
ggOut <- ggOut + theme(title = element_text(size = 20))
ggOut <- ggOut + theme(axis.title = element_blank())
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
ggOut <- ggOut + theme(axis.text.x = element_text(size = 12))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 12))
ggOut <- ggOut + theme(legend.position = "none")
ggOut <- ggOut + theme(plot.background = element_blank())
ggOut <- ggOut + theme(panel.background = element_blank())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + geom_label(aes(x = def, label = scales::percent(round(out$res, digits = 2))), size = 4)
ggOut
}
Gen909090Plot_2016(yr = 2016)
###
head(CalibOut)
test <- CalibOut
plhiv_2010 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2010, "value"])
plhiv_2011 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2011, "value"])
plhiv_2012 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2012, "value"])
plhiv_2013 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2013, "value"])
plhiv_2014 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2014, "value"])
plhiv_2015 <- mean(test[test$source == "model" & test$indicator == "PLHIV" & test$year == 2015, "value"])
plhiv <- c(plhiv_2010, plhiv_2011, plhiv_2012, plhiv_2013, plhiv_2014, plhiv_2015)
inc <- as.double(MasterData$incidence[2,3:8])
inc
rate <- inc / plhiv
year <- seq(2010, 2015, 1)
df <- data.frame(year, rate)
graphics.off(); quartz.options(w = 5, h = 3)
ggplot(df, aes(x = year, y = rate)) +
geom_line() +
ggtitle("Transmission Rate", subtitle = "New Infections / Total PLHIV")
quartz.save(file = "~/Desktop/fig/transmission-rate.pdf", type = "pdf")
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