#This will be called through source()
#function to clean up the column or row names,
format.names <- function(x,replace = "X",...){
gsub(replace,'',x)
}
#Recruitment
#maxart.study.pop <- recruit.study.clients(chunk.datalist.test)
#Dummy with no real recruitment
two.weeks <- 14/365.245
twelve.months <- 12/12
six.months <- 6/12
init.immediate <- 0
init.not.eligible <- -6/12
more.months <- twelve.months + six.months
max.swazi.sim.summary.creator <- function(sim.datalist = chunk.datalist.test){
#creator a range of summary statistics
sim.datalist$ptable$primary <- 0
#get the MaxART population group
maxart.study.pop <- subset(sim.datalist$ptable, pfacility != "Not Hhohho")
df.rw <- nrow(maxart.study.pop)
#Dummy when eligible
sample.eligible <- sample(c(two.weeks, twelve.months, six.months,
init.immediate, more.months,
init.not.eligible, NA), replace = TRUE, df.rw)
maxart.study.pop$art.eligible <- maxart.study.pop$TreatTime + sample.eligible
#dummy 40% as primary
count.prim <- round(0.4 * nrow(maxart.study.pop), 0)
maxart.study.pop$primary[which(maxart.study.pop$ID %in% sample(maxart.study.pop$ID, count.prim))] <- 1
all.prim.maxart <- sum(maxart.study.pop$primary==1)
all.maxart <- nrow(maxart.study.pop)
#Get the two primary and all clients distinction
max.art.ret.study.pop.prim <- maxart.study.pop %>%
filter(TreatTime !=Inf & primary ==1) %>%
as.data.frame
max.art.ret.study.pop.all <- maxart.study.pop %>%
filter(TreatTime !=Inf)
#alldf
chunk.datalist.test.prim <- sim.datalist
chunk.datalist.test.prim$ptable <- max.art.ret.study.pop.prim
chunk.datalist.test.all <- sim.datalist
chunk.datalist.test.all$ptable <- max.art.ret.study.pop.all
### ART coverage ####################
max.all.prim.art.coverage.var <- maxart.study.pop %>%
filter(TreatTime!=Inf) %>%
summarise(all.prim = sum(primary==1, na.rm = TRUE)/all.prim.maxart,
all.all = n()/all.maxart,
b4.elig.prim = sum(TreatTime < art.eligible & primary==1, na.rm = TRUE)/all.prim.maxart,
b4.elig.all = sum(TreatTime < art.eligible, na.rm = TRUE )/all.maxart,
within2wks.prim = sum(TreatTime>=art.eligible &
TreatTime < (TreatTime+two.weeks) & primary==1, na.rm = TRUE)/all.prim.maxart,
within2wks.all = sum(TreatTime>=art.eligible &
TreatTime < (TreatTime+two.weeks), na.rm = TRUE )/all.maxart,
amongelig.prim = sum(TreatTime > art.eligible & primary==1, na.rm = TRUE)/all.prim.maxart,
amongelig.all = sum(TreatTime > art.eligible, na.rm = TRUE )/all.maxart,
within12m.prim = sum(TreatTime==art.eligible &
TreatTime < (TreatTime+twelve.months) & primary==1, na.rm = TRUE)/all.prim.maxart,
within12m.all = sum(TreatTime==art.eligible &
TreatTime < (TreatTime+twelve.months) , na.rm = TRUE)/all.maxart,
within6m.prim = sum(TreatTime==art.eligible &
TreatTime < (TreatTime+six.months) & primary==1, na.rm = TRUE)/all.prim.maxart,
within6m.all = sum(TreatTime==art.eligible &
TreatTime < (TreatTime+six.months) , na.rm = TRUE)/all.maxart
)
prim.art.init <- dplyr::select(max.all.prim.art.coverage.var, contains(".prim"))
all.art.init <- dplyr::select(max.all.prim.art.coverage.var, contains(".all"))
max.art.initiated.tar.values <- as.numeric(cbind(prim.art.init*100, all.art.init*100))
# #max.art.coverage.tar <- data.frame(t(prim.art.init)*100, t(all.art.init)*100) %>%
# # setNames(c("PrimaryClients","AllClients"))
# #row.names(max.art.coverage.tar) <- row.names(max.art.initiated.all)
#
#when do we want the retention time
maxart.ret.timepoint <- difftime(maxart.endtime, sim.start.full, units = "days")/365.242
maxart.ret.timepoint <- round(as.numeric(maxart.ret.timepoint),0)
maxart.starttime.ret <- round(as.numeric(difftime(maxart.starttime ,sim.start.full, units = "days")/365),0)
#### Max Retention ####################################
max.ret.tar <- length(row.names(max.art.retention.all))
max.ret.tar.prim <- rep(NA, max.ret.tar)
max.ret.tar.all <- rep(NA, max.ret.tar)
max.ret.tar.list <- c(36, 6, 12)
ret.age.group <- c(18, 150)
for(ret in 1:max.ret.tar){
ret.all.prim = ART.retention(datalist = chunk.datalist.test.prim,
agegroup = ret.age.group, #all ages
ARTtimewindow = c(maxart.starttime.ret, maxart.ret.timepoint),
retentiontimeMonths = max.ret.tar.list[ret], #6 months default
site="All")$percentage[1]
ret.all.all = ART.retention(datalist = chunk.datalist.test.all,
agegroup = ret.age.group, #all ages
ARTtimewindow = c(maxart.starttime.ret, maxart.ret.timepoint),
retentiontimeMonths = max.ret.tar.list[ret], #6 months default
site="All")$percentage[1]
max.ret.tar.prim[ret] <- ret.all.prim
max.ret.tar.all[ret] <- ret.all.all
}
max.art.retention.tar.values <- c(max.ret.tar.prim, max.ret.tar.all)
# # #max.art.ret.tar <- data.frame(PrimaryClients = max.ret.tar.prim,
# # # AllClients = max.ret.tar.all)
# #
# # #row.names(max.art.ret.tar) <- row.names(max.art.retention.all)
# #
# # ############### Viral Load none supresssion ############################################
max.vl.sup.tar.dim <- length(row.names(max.vl.none.suppression.all))
max.vl.sup.prim <- rep(NA, max.vl.sup.tar.dim)
max.vl.sup.all <- rep(NA, max.vl.sup.tar.dim)
max.vl.sup.list <- c(6, 12)
for(vl in 1:max.vl.sup.tar.dim){
vl.sup.prim = 100 - vl.suppressed(datalist = chunk.datalist.test.prim,
timepoint = maxart.ret.timepoint, vlcutoff = 1000,
lessmonths = max.vl.sup.list[vl], site="All")$percentage[[1]]
vl.sup.all = 100 - vl.suppressed(datalist = chunk.datalist.test.all,
timepoint = maxart.ret.timepoint, vlcutoff = 1000,
lessmonths = max.vl.sup.list[vl], site="All")$percentage[[1]]
max.vl.sup.prim[vl] <- ifelse(is.null(vl.sup.prim), NA, vl.sup.prim)
max.vl.sup.all[vl] <- ifelse(is.null(vl.sup.all), NA, vl.sup.all)
}
max.vl.none.suppression.tar.values <- c(max.vl.sup.prim, max.vl.sup.all)
# #
# # #max.vl.sup.tar <- data.frame(PrimaryClients = max.vl.sup.prim,
# # # AllClients = max.vl.sup.all)
# # #row.names(max.vl.sup.tar) <- row.names(max.vl.none.suppression.all)
# #
# #
# #Mortality AIDS related and not
max.mort.study.pop.prim <- maxart.study.pop %>%
summarise(mort.prim = sum(primary == 1 & TOD != Inf & TOD > maxart.starttime & TOD < maxart.endtime, na.rm = TRUE) / n(),
mort.all = sum(TOD != Inf & TOD > maxart.starttime & TOD < maxart.endtime, na.rm = TRUE) / n(),
mort.aids.rel.prim = sum(primary == 1 & TOD != Inf & AIDSDeath == 1 &
TOD > maxart.starttime & TOD < maxart.endtime, na.rm = TRUE) / n(),
mort.aids.rel.all = sum(TOD != Inf & AIDSDeath == 1 &
TOD > maxart.starttime & TOD < maxart.endtime, na.rm = TRUE) / n() )
prim.mortality <- dplyr::select(max.mort.study.pop.prim, contains(".prim"))
all.mortality <- dplyr::select(max.mort.study.pop.prim, contains(".all"))
max.mortality.tar.values <- as.numeric(cbind(prim.mortality * 100, all.mortality * 100))
# #
# # #max.mortality.tar <- data.frame(t(prim.mortality)*100, t(all.mortality)*100) %>%
# # # setNames(c("PrimaryClients","AllClients"))
# # #row.names(max.mortality.tar) <- row.names(max.mortality.all)
# #
# ##### Growth rate ########################################
gr.year.list <- as.numeric(format.names(names(swazi.growth.rate), replace = "X"))
swazi.growth.rate.tar.values <- rep(NA, length(gr.year.list))
#Growth rate is calculated per year.
for(i in 1:length(gr.year.list)){
from.time <- gr.year.list[i] - 1 - sim.start
to.time <- from.time + 1
swazi.growth.rate.tar.values[i] <- pop.growth.calculator(datalist = sim.datalist,
timewindow = c(from.time, to.time))
}
# #
# # # #Set names for the growth rate
# # # #swazi.growth.rate.diff <- swazi.growth.rate - swazi.sim.growth.rate ###############################
# # #
# # ##### Incidence for multiple years, 15-49 yrs, time = March each year #############################
inci.year.list <- as.numeric(format.names(names(swazi.inci.15.49), replace = "X"))
swazi.inci.15.49.tar.values <- rep(NA, length(inci.year.list))
for(i in 1:length(inci.year.list)){
time.start <- inci.year.list[i] - 1 - sim.start
time.end <- time.start + 1
swazi.sim.inci <- incidence.calculator(datalist = sim.datalist,
agegroup = c(15, 49), # <= hence not using 50.
timewindow = c(time.start, time.end),
only.active = "No")
swazi.inci.15.49.tar.values[i] <- swazi.sim.inci$incidence[3] * 100
}
# #
# # #swazi.inci.15.49.diff <- swazi.inci.15.49 - swazi.sim.inci.15.49 ######################
# #
# # #Incidence for multiple ages, start and end time set ###############################################
inci.age2011.list <- format.names(row.names(swazi.inci.2011), replace = "A")
swazi.sim.inci.2011.f <- rep(NA, length(inci.age2011.list))
swazi.sim.inci.2011.m <- rep(NA, length(inci.age2011.list))
time.start.2011 <- as.numeric(difftime(as.Date("2010-12-01") ,sim.start.full, units = "days")/365.245)
time.end.2011 <- as.numeric(difftime(as.Date("2011-06-30") ,sim.start.full, units = "days")/365.245)
for(i in 1:length(inci.age2011.list)){
split.list.age2011 <- as.numeric(unlist(strsplit(inci.age2011.list[i], "[.]")))
age.lower.2011 <- split.list.age2011[1]
age.upper.2011 <- split.list.age2011[2]
swazi.age.sim.inci <- incidence.calculator(datalist = sim.datalist,
agegroup = c(age.lower.2011, age.upper.2011), # <= hence using exact.
timewindow = c(time.start.2011, time.end.2011),
only.active = "No")
#Gender 0 <- male : 1 <- female
swazi.sim.inci.2011.m[i] <- swazi.age.sim.inci$incidence[1] * 100
swazi.sim.inci.2011.f[i] <- swazi.age.sim.inci$incidence[2] * 100
}
swazi.inci.2011.tar.values <- c(swazi.sim.inci.2011.f,swazi.sim.inci.2011.m)
# #
# # #swazi.sim.inci.2011.fm <- data.frame(swazi.sim.inci.2011.f, swazi.sim.inci.2011.m)
# # #swazi.inci.age.2011.diff <- swazi.inci.2011[,1:2] - swazi.sim.inci.2011.fm
# # ##################################################################################################
# #
# #
# # ######### Prevalence for multiple ages. End time 2007-March-31 ###################################
prev.age2007.list <- format.names(row.names(swazi.prev.2007), replace = "A")
swazi.sim.prev.2007.len <- length(prev.age2007.list)
swazi.sim.prev.2007.f <- rep(NA, swazi.sim.prev.2007.len)
swazi.sim.prev.2007.m <- rep(NA, swazi.sim.prev.2007.len)
swazi.sim.prev.2007.fm <- rep(NA, swazi.sim.prev.2007.len)
time.end.2007 <- as.numeric(difftime(as.Date("2007-03-31") ,sim.start.full, units = "days")/365.245)
for(i in 1:swazi.sim.prev.2007.len){
split.list.age2007 <- as.numeric(unlist(strsplit(prev.age2007.list[i], "[.]")))
age.lower.2007 <- split.list.age2007[1]
age.upper.2007 <- split.list.age2007[2]
swazi.age.sim.prev <- prevalence.calculator(datalist = sim.datalist,
agegroup = c(age.lower.2007, age.upper.2007),
timepoint = time.end.2007)
#Gender 0 <- male : 1 <- female
swazi.sim.prev.2007.m[i] <- swazi.age.sim.prev$pointprevalence[1] * 100
swazi.sim.prev.2007.f[i] <- swazi.age.sim.prev$pointprevalence[2] * 100
swazi.sim.prev.2007.fm[i] <- swazi.age.sim.prev$pointprevalence[3] * 100
}
swazi.prev.2007.tar.values <- c(swazi.sim.prev.2007.f,
swazi.sim.prev.2007.m,
swazi.sim.prev.2007.fm)
# #
# # #swazi.prev.age.2007.diff <- swazi.prev.2007[,1:3] - swazi.sim.prev.2007
# # ##################################################################################################
# #
# # #Prevalence for multiple ages and July 2006 and March 2007 [SDHS 2006-07] ########################
swazi.hhohho.prev.age.list <- format.names(row.names(hhohho.prev), replace = "A")
swazi.hhohho.sim.prev.age.yr.len <- length(swazi.hhohho.prev.age.list)
swazi.hhohho.sim.prev.age.yr.f <- rep(NA, swazi.hhohho.sim.prev.age.yr.len)
swazi.hhohho.sim.prev.age.yr.m <- rep(NA, swazi.hhohho.sim.prev.age.yr.len)
swazi.hhohho.sim.prev.age.yr.fm <- rep(NA, swazi.hhohho.sim.prev.age.yr.len)
swazi.hhohho.prev.df <- sim.datalist
swazi.hhohho.prev.df.ptable <- subset(swazi.hhohho.prev.df$ptable, pfacility!="Not Hhohho")
swazi.hhohho.prev.df$ptable <- swazi.hhohho.prev.df.ptable
for(i in 1:swazi.hhohho.sim.prev.age.yr.len){
split.list.id <- as.numeric(unlist(strsplit(swazi.hhohho.prev.age.list[i], "[.]")))
time.end.age.yr <- round(as.numeric(difftime(as.Date(paste0("2007-03-31")),
sim.start.full, units = "days")/365.245),0)
age.lower.age.yr <- split.list.id[1]
age.upper.age.yr <- split.list.id[2]
swazi.hhohho.sim.age.yr.sim.prev <- prevalence.calculator(datalist = swazi.hhohho.prev.df,
agegroup = c(age.lower.age.yr, age.upper.age.yr),
timepoint = time.end.age.yr)
#Gender 0 <- male : 1 <- female
swazi.hhohho.sim.prev.age.yr.m[i] <- swazi.hhohho.sim.age.yr.sim.prev$pointprevalence[1] * 100
swazi.hhohho.sim.prev.age.yr.f[i] <- swazi.hhohho.sim.age.yr.sim.prev$pointprevalence[2] * 100
swazi.hhohho.sim.prev.age.yr.fm[i] <- swazi.hhohho.sim.age.yr.sim.prev$pointprevalence[3] * 100
}
swazi.hhohho.prev.tar.values <- c(swazi.hhohho.sim.prev.age.yr.f,
swazi.hhohho.sim.prev.age.yr.m,
swazi.hhohho.sim.prev.age.yr.fm)
# #
# #
# #Prevalence for multiple ages and time Year ends 31 March Year ##################################
prev.age.year.list <- format.names(row.names(swazi.prev.age.year), replace = "A")
swazi.sim.prev.age.yr.len <- length(prev.age.year.list)
swazi.sim.prev.age.yr.f <- rep(NA, swazi.sim.prev.age.yr.len)
swazi.sim.prev.age.yr.m <- rep(NA, swazi.sim.prev.age.yr.len)
for(i in 1:swazi.sim.prev.age.yr.len){
split.list.id <- as.numeric(unlist(strsplit(prev.age.year.list[i], "[.]")))
time.end.age.yr <- as.numeric(difftime(as.Date(paste0(split.list.id[3],"-03-31")) ,
sim.start.full, units = "days")/365.245)
age.lower.age.yr <- split.list.id[1]
age.upper.age.yr <- split.list.id[2]
swazi.sim.age.yr.sim.prev <- prevalence.calculator(datalist = sim.datalist,
agegroup = c(age.lower.age.yr, age.upper.age.yr),
timepoint = time.end.age.yr)
#Gender 0 <- male : 1 <- female
swazi.sim.prev.age.yr.m[i] <- swazi.sim.age.yr.sim.prev$pointprevalence[1] * 100
swazi.sim.prev.age.yr.f[i] <- swazi.sim.age.yr.sim.prev$pointprevalence[2] * 100
}
swazi.prev.age.year.tar.values <- c(swazi.sim.prev.age.yr.f, swazi.sim.prev.age.yr.m)
# #
# # #swazi.prev.age.age.yr.diff <- swazi.prev.age.year[,1:2] - swazi.sim.prev.age.yr
# # ##################################################################################################
# #
# #
# # #Swazi Age difference 2011-06-30
hhohho.age.dif.time <- round(as.numeric(difftime(as.Date("2007-03-30") ,
sim.start.full, units = "days")/365.245),0)
chunk.datalist.test.hhohho <- sim.datalist
hhohho.df <- subset(sim.datalist$ptable, pfacility !="Hhohho")
chunk.datalist.test.hhohho$ptable <- hhohho.df
agemix.df.hhohho <- agemix.df.maker(chunk.datalist.test.hhohho)
pattern.hhohho <- pattern.modeller(dataframe = agemix.df.hhohho, agegroup = c(18, 50),
timepoint = hhohho.age.dif.time, timewindow = 1, start = FALSE)[[1]]
pattern.hhohho <- as.data.frame(pattern.hhohho)
pattern.hhohho.df.m <- as.data.frame(subset(pattern.hhohho, Gender == "male"))
pattern.hhohho.df.f <- as.data.frame(subset(pattern.hhohho, Gender == "female"))
hhohho.mean.m.18.50.AD <- as.numeric(mean(pattern.hhohho.df.m$AgeGap))
hhohho.median.m.18.50.AD <- as.numeric(median(pattern.hhohho.df.m$AgeGap))
hhohho.mean.w.18.50.AD <- as.numeric(mean(pattern.hhohho.df.f$AgeGap))
hhohho.median.w.18.50.AD <- as.numeric(median(pattern.hhohho.df.f$AgeGap))
hhohho.mean.fm.18.50.AD <- as.numeric(mean(pattern.hhohho$AgeGap))
hhohho.median.fm.18.50.AD <- as.numeric(median(pattern.hhohho$AgeGap))
swazi.hhohho.AD.tar.values <- c(hhohho.mean.w.18.50.AD, hhohho.median.w.18.50.AD,
hhohho.mean.m.18.50.AD, hhohho.median.m.18.50.AD,
hhohho.mean.fm.18.50.AD, hhohho.median.fm.18.50.AD)
#
#Swazi Age difference 2011-06-30
swazi.age.dif.time <- as.numeric(difftime(as.Date("2011-06-30") ,sim.start.full, units = "days")/365.245)
agemix.df <- agemix.df.maker(sim.datalist)
swazi.pattern <- pattern.modeller(dataframe = agemix.df, agegroup = c(18, 50),
timepoint = swazi.age.dif.time, timewindow = 1, start = FALSE)[[1]]
swazi.pattern.df <- as.data.frame(swazi.pattern)
swazi.mean.fm.18.50.AD <- as.numeric(mean(swazi.pattern.df$AgeGap))
swazi.median.fm.18.50.AD <- as.numeric(median(swazi.pattern.df$AgeGap))
swazi.AD.tar.values <- c(swazi.mean.fm.18.50.AD, swazi.median.fm.18.50.AD)
# Swazi ART retention for multiple ages and two age groups
ret.age.year.list <- as.numeric(format.names(names(swazi.art.retention), replace = "X"))
swazi.sim.ret.age.yr.len <- length(ret.age.year.list)
swazi.sim.ret.age.yr.l15.6 <- rep(NA, swazi.sim.ret.age.yr.len)
swazi.sim.ret.age.yr.g15.6 <- rep(NA, swazi.sim.ret.age.yr.len)
swazi.sim.ret.age.yr.l15.12 <- rep(NA, swazi.sim.ret.age.yr.len)
swazi.sim.ret.age.yr.g15.12 <- rep(NA, swazi.sim.ret.age.yr.len)
less15 <- c(0,14)
greater15 <- c(15,150)
for(i in 1:swazi.sim.ret.age.yr.len){
uptime.lim <- ret.age.year.list[i] - sim.start
#compute retention and get the overall value (ignore gender)
swazi.sim.ret.age.yr.l15.6.var <- ART.retention(datalist = sim.datalist,
agegroup = less15,
ARTtimewindow = c(0, uptime.lim),
retentiontimeMonths = 6, #6 months default
site="All")
swazi.sim.ret.age.yr.l15.6[i] <- swazi.sim.ret.age.yr.l15.6.var$percentage[
is.na(swazi.sim.ret.age.yr.l15.6.var$Gender)]
swazi.sim.ret.age.yr.g15.6.var <- ART.retention(datalist = sim.datalist,
agegroup = greater15,
ARTtimewindow = c(0, uptime.lim),
retentiontimeMonths = 6, #6 months default
site="All")
swazi.sim.ret.age.yr.g15.6[i] <- swazi.sim.ret.age.yr.g15.6.var$percentage[
is.na(swazi.sim.ret.age.yr.g15.6.var$Gender)]
swazi.sim.ret.age.yr.l15.12.var <- ART.retention(datalist = sim.datalist,
agegroup = less15,
ARTtimewindow = c(0, uptime.lim),
retentiontimeMonths = 12, #6 months default
site="All")
swazi.sim.ret.age.yr.l15.12[i] <- swazi.sim.ret.age.yr.l15.12.var$percentage[
is.na(swazi.sim.ret.age.yr.l15.12.var$Gender)]
swazi.sim.ret.age.yr.g15.12.var <- ART.retention(datalist = sim.datalist,
agegroup = greater15,
ARTtimewindow = c(0, uptime.lim),
retentiontimeMonths = 12, #6 months default
site="All")
swazi.sim.ret.age.yr.g15.12[i] <- swazi.sim.ret.age.yr.g15.12.var$percentage[
is.na(swazi.sim.ret.age.yr.g15.12.var$Gender)]
}
swazi.sim.ret.age.yr <- data.frame(matrix(NA, nrow = 0, ncol = swazi.sim.ret.age.yr.len))
swazi.sim.ret.age.yr <- rbind(swazi.sim.ret.age.yr,
swazi.sim.ret.age.yr.l15.6, swazi.sim.ret.age.yr.g15.6,
swazi.sim.ret.age.yr.l15.12, swazi.sim.ret.age.yr.g15.12)
names(swazi.sim.ret.age.yr) <- names(swazi.art.retention)
#
swazi.art.retention.tar.values <- as.numeric(unlist(swazi.sim.ret.age.yr))
# #
# # #####################################################################################################
# #
# # #ART coverage for multiple Years and 15+ yo. Year ends 31 March Year
art.coverage.year.list <- as.numeric(format.names(row.names(swazi.art.coverage), replace = "15.over."))
art.coverage.year.len <- length(art.coverage.year.list)
art.coverage.year.f <- rep(NA, art.coverage.year.len)
art.coverage.year.m <- rep(NA, art.coverage.year.len)
art.coverage.year.fm <- rep(NA, art.coverage.year.len)
for(i in 1:art.coverage.year.len){
time.end.cov <- as.numeric(art.coverage.year.list[i] - sim.start)
ARTcov <- ART.coverage.calculator(datalist = sim.datalist, agegroup = c(18, 50),
timepoint = time.end.cov, site="All")
#Gender 0 <- male : 1 <- female
art.coverage.year.m[i] <- ARTcov$ART.coverage[1] * 100
art.coverage.year.f[i] <- ARTcov$ART.coverage[2] * 100
art.coverage.year.fm[i] <- ARTcov$ART.coverage[3] * 100
}
swazi.art.coverage.tar.values <- c(art.coverage.year.f, art.coverage.year.m, art.coverage.year.fm)
#
#collect all the summary values
sim.summary <- c(max.art.initiated.tar.values, max.art.retention.tar.values,
max.vl.none.suppression.tar.values, max.mortality.tar.values,
swazi.growth.rate.tar.values, swazi.inci.15.49.tar.values,
swazi.inci.2011.tar.values, swazi.prev.2007.tar.values,
swazi.prev.age.year.tar.values, swazi.hhohho.prev.tar.values,
swazi.AD.tar.values, swazi.hhohho.AD.tar.values,
swazi.art.retention.tar.values, swazi.art.coverage.tar.values)
# #Testing
#sim.summary <- c(max.art.retention.tar.values, Sys.getpid())
return(sim.summary)
}
#swazi.art.coverage.year.diff <- swazi.art.coverage[,1:2] - swazi.sim.art.coverage.year
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