# This file will contain all the relevant assumptions for individual countries.
# At first this will be based upon the marrakech.csv data presented.
# But, we will eventually pivot to relying entirely on data entered into the model
# Will need some testing architecture to sort that out.
# i.e. a test to make sure values don't conflict - will need to be relatively clever too.
# source("calibration-data.R")
# uCountry <- "Kenya"
# countryData <- GetCountryData("Kenya")
# need to make judgement calls on each country. Regarding the data.
# So after GetCountryData() we need a MakeAssumptions() [to fill in the gaps]
MakeAssumptions <- function(uCountry, countryData) {
if (uCountry == "Kenya") {
# Do assumptiony stuff then.
countryData <- countryData$calib
# For now propogate assumptions over time.
## DIAGNOSES ##
# In Kenya, we know that 46.9% of PLHIV were diagnosed in 2012.
# Spectrum tells us that there are 1,327,788 PLHIV in 2012.
# 1,327,788 * 0.469 = 622,733
# However, we do not know the values for other years.
# We will assume that it holds for years prior to 2012, but not after.
# So 46.9% aware of status in 2012
k.propDiag <- 0.469 # KAIS2012
# We assume that this carries over time
k.plhiv <- countryData[countryData$indicator == "PLHIV" & countryData$year %in% c(2010, 2011), "value"]
# Expanding over timeframe
country <- "Kenya"
indicator <- "PLHIV Diagnosed"
year <- c(2010, 2011)
value <- k.plhiv * k.propDiag
weight <- "red"
new.diag <- data.frame(country, indicator, year, value, weight)
# test3 <- rbind(countryData, new.diag)
# ggplot(test3, aes(x = year, y = value)) + geom_bar(aes(fill = indicator), stat = "identity", position = "dodge")
# TEST TEST TEST
# dplyr::filter(test3, indicator == "PLHIV")$value
# dplyr::filter(test3, indicator == "PLHIV Diagnosed")$value
# dplyr::filter(test3, indicator == "PLHIV on ART")$value
# return the 'new' data.frame (pass to next lines)
## CARE ##
# From Marrakech data we know that in 2015, 57% of PLHIV were in care.
# But that is from using the Kenyan estimate of '# PLHIV'
# If we use our Spectrum estimate for PLHIV, we find that 67% of PLHIV were in care in 2015.
# But, both of these values, cause issues, because in 2012, only 47% of PLHIV were diagnosed...
# Therefore prior to 2015, fewer individuals must have been in care.
# Building data.frame
country <- "Kenya"
indicator <- "PLHIV in Care"
year <- seq(2010, 2015, 1)
# value <- countryData[countryData$indicator == "PLHIV","value"] * 0.57
value <- countryData[countryData$indicator == "PLHIV","value"] * 0.67
weight <- "red"
new.care <- data.frame(country, indicator, year, value, weight)
# test4 <- rbind(countryData, new.diag, new.care)
# ggplot(test4, aes(x = year, y = value)) + geom_point(aes(color = indicator))
# Caveats
# In 2015, PLHIV in CARE < PLHIV on ART. This gets overwritten by Marrakech data in any case.
# Might just truncate the data from 2015 and leave the rest.
assumptions.return <- rbind(new.diag, new.care)
} else {
stop("No code written for generating assumptions on other countries aside from Kenya.")
}
# Return the assumptions data.frame taking the standard form as before.
assumptions.return
}
# Careful not to return the countryData too.
# MakeAssumptions("Kenya", countryData)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.