# MSM -----------------------------------------------------------------
#' @title Initialization Module
#'
#' @description This function initializes the master \code{dat} object on which
#' data are stored, simulates the initial state of the network, and
#' simulates disease status and other attributes.
#'
#' @param x An \code{EpiModel} object of class \code{\link{netest}}.
#' @param param An \code{EpiModel} object of class \code{\link{param_msm}}.
#' @param init An \code{EpiModel} object of class \code{\link{init_msm}}.
#' @param control An \code{EpiModel} object of class \code{\link{control_msm}}.
#' @param s Simulation number, used for restarting dependent simulations.
#'
#' @return
#' This function returns the updated \code{dat} object with the initialized values
#' for demographics and disease-related variables.
#'
#' @export
#' @keywords module msm
#'
initialize_msm <- function(x, param, init, control, s) {
## Master Data List Setup ##
dat <- list()
dat$param <- param
dat$init <- init
dat$control <- control
## Network Setup ##
# Initial network simulations
dat$nw <- list()
for (i in 1:3) {
dat$nw[[i]] <- simulate(x[[i]]$fit, basis = x[[i]]$fit$newnetwork)
}
nw <- dat$nw
# Pull Network parameters
dat$nwparam <- list()
for (i in 1:3) {
dat$nwparam[i] <- list(x[[i]][-which(names(x[[i]]) == "fit")])
}
# Convert to tergmLite method
dat <- init_tergmLite(dat)
## Nodal Attributes Setup ##
dat$attr <- param$netstats$attr
num <- network.size(nw[[1]])
dat$attr$active <- rep(1, num)
dat$attr$arrival.time <- rep(1, num)
dat$attr$uid <- 1:num
# Circumcision
rates <- param$circ.prob[dat$attr$race]
dat$attr$circ <- rbinom(length(rates), 1, rates)
# Insertivity Quotient
ins.quot <- rep(NA, num)
role.class <- dat$attr$role.class
ins.quot[role.class == 0] <- 1
ins.quot[role.class == 1] <- 0
ins.quot[role.class == 2] <- runif(sum(role.class == 2))
dat$attr$ins.quot <- ins.quot
# HIV-related attributes
dat <- init_status_msm(dat)
# STI Status
dat <- init_sti_msm(dat)
# PrEP-related attributes
dat$attr$prepClass <- rep(NA, num)
dat$attr$prepElig <- rep(NA, num)
dat$attr$prepStat <- rep(0, num)
dat$attr$prepStartTime <- rep(NA, num)
dat$attr$prepLastRisk <- rep(NA, num)
dat$attr$prepLastStiScreen <- rep(NA, num)
## Other Setup ##
dat$stats <- list()
dat$stats$nwstats <- list()
dat$temp <- list()
dat$epi <- list()
# Prevalence Tracking
dat$temp$max.uid <- num
dat <- prevalence_msm(dat, at = 1)
# Setup Partner List
plist <- cbind(dat$el[[1]], ptype = 1)
plist <- rbind(plist, cbind(dat$el[[2]], ptype = 2))
plist <- cbind(plist, start = 1, stop = NA)
colnames(plist)[1:2] <- c("p1", "p2")
dat$temp$plist <- plist
# Clinical history
if (dat$control$save.clin.hist == TRUE) {
dat <- save_clin_hist(dat, at = 1)
}
# Network statistics
if (dat$control$save.nwstats == TRUE) {
dat <- calc_nwstats(dat, at = 1)
}
# dat$param$netstats <- NULL
class(dat) <- "dat"
return(dat)
}
#' @title Initialize the HIV status of persons in the network
#'
#' @description Sets the initial individual-level disease status of persons
#' in the network, as well as disease-related attributes for
#' infected persons.
#'
#' @param dat Data object created in initialization module.
#'
#' @export
#' @keywords initiation utility msm
#'
init_status_msm <- function(dat) {
num <- sum(dat$attr$active == 1)
# Sub in diag.status from model for status
status <- dat$attr$diag.status
# Late (AIDS-stage) tester type
rates <- dat$param$hiv.test.late.prob[dat$attr$race]
dat$attr$late.tester <- rbinom(length(rates), 1, rates)
# Treatment trajectory
tt.traj <- rep(NA, num)
races <- sort(unique(dat$attr$race))
for (i in races) {
ids.race <- which(dat$attr$race == i)
tt.traj[ids.race] <- sample(1:3, length(ids.race), TRUE,
c(dat$param$tt.part.supp[i],
dat$param$tt.full.supp[i],
dat$param$tt.dur.supp[i]))
}
dat$attr$tt.traj <- tt.traj
## Infection-related attributes
dat$attr$status <- status
idsInf <- which(status == 1)
age <- dat$attr$age
min.ages <- min(dat$param$netstats$demog$ages)
time.sex.active <- pmax(1, round((365/7)*age[idsInf] - (365/7)*min.ages, 0))
min.hiv.time <- round(dat$param$vl.acute.rise.int + dat$param$vl.acute.fall.int)
max.hiv.time <- dat$param$vl.aids.onset.int
time.infected <- round(pmax(min.hiv.time,
pmin(time.sex.active,
sample(min.hiv.time:max.hiv.time, length(idsInf), TRUE))))
dat$attr$inf.time <- rep(NA, num)
dat$attr$inf.time[idsInf] <- -time.infected
dat$attr$stage <- rep(NA, num)
dat$attr$stage.time <- rep(NA, num)
dat$attr$aids.time <- rep(NA, num)
dat$attr$stage[idsInf] <- 3
dat$attr$stage.time[idsInf] <- time.infected - min.hiv.time
dat$attr$diag.stage <- rep(NA, num)
dat$attr$diag.stage[idsInf] <- dat$attr$stage[idsInf]
dat$attr$vl <- rep(NA, num)
dat$attr$vl[idsInf] <- dat$param$vl.set.point
dat$attr$vl.last.usupp <- rep(NA, num)
dat$attr$vl.last.supp <- rep(NA, num)
dat$attr$diag.time <- rep(NA, num)
dat$attr$diag.time[idsInf] <- dat$attr$inf.time[idsInf] + round(mean(1/dat$param$hiv.test.rate))
dat$attr$last.neg.test <- rep(NA, num)
dat$attr$tx.status <- rep(NA, num)
dat$attr$tx.status[idsInf] <- 0
dat$attr$cuml.time.on.tx <- rep(NA, num)
dat$attr$cuml.time.on.tx[idsInf] <- 0
dat$attr$cuml.time.off.tx <- rep(NA, num)
dat$attr$cuml.time.off.tx[idsInf] <- time.infected
dat$attr$tx.period.first <- rep(NA, num)
dat$attr$tx.period.last <- rep(NA, num)
dat$attr$tx.init.time <- rep(NA, num)
dat$attr$count.trans <- rep(0, num)
dat$attr$num.neg.tests <- rep(0, length(status))
return(dat)
}
#' @title Initialize the STI status of persons in the network
#'
#' @description Sets the initial individual-level disease status of persons
#' in the network, as well as disease-related attributes for
#' infected persons.
#'
#' @param dat Data object created in initialization module.
#'
#' @export
#' @keywords initiation utility msm
#'
init_sti_msm <- function(dat) {
role.class <- dat$attr$role.class
num <- length(role.class)
idsUreth <- which(role.class %in% c(0, 2))
idsRect <- which(role.class %in% c(1, 2))
uGC <- rGC <- rep(0, num)
uCT <- rCT <- rep(0, num)
# Initialize GC infection at both sites
idsUGC <- sample(idsUreth, size = round(dat$init$prev.ugc * num), FALSE)
uGC[idsUGC] <- 1
idsRGC <- sample(setdiff(idsRect, idsUGC), size = round(dat$init$prev.rgc * num), FALSE)
rGC[idsRGC] <- 1
dat$attr$rGC <- rGC
dat$attr$uGC <- uGC
dat$attr$rGC.sympt <- dat$attr$uGC.sympt <- rep(NA, num)
dat$attr$rGC.sympt[rGC == 1] <- rbinom(sum(rGC == 1), 1, dat$param$rgc.sympt.prob)
dat$attr$uGC.sympt[uGC == 1] <- rbinom(sum(uGC == 1), 1, dat$param$ugc.sympt.prob)
dat$attr$rGC.infTime <- dat$attr$uGC.infTime <- rep(NA, length(dat$attr$active))
dat$attr$rGC.infTime[rGC == 1] <- 1
dat$attr$uGC.infTime[uGC == 1] <- 1
dat$attr$rGC.timesInf <- rep(0, num)
dat$attr$rGC.timesInf[rGC == 1] <- 1
dat$attr$uGC.timesInf <- rep(0, num)
dat$attr$uGC.timesInf[uGC == 1] <- 1
dat$attr$rGC.tx <- dat$attr$uGC.tx <- rep(NA, num)
dat$attr$rGC.tx.prep <- dat$attr$uGC.tx.prep <- rep(NA, num)
# Initialize CT infection at both sites
idsUCT <- sample(idsUreth, size = round(dat$init$prev.uct * num), FALSE)
uCT[idsUCT] <- 1
idsRCT <- sample(setdiff(idsRect, idsUCT), size = round(dat$init$prev.rct * num), FALSE)
rCT[idsRCT] <- 1
dat$attr$rCT <- rCT
dat$attr$uCT <- uCT
dat$attr$rCT.sympt <- dat$attr$uCT.sympt <- rep(NA, num)
dat$attr$rCT.sympt[rCT == 1] <- rbinom(sum(rCT == 1), 1, dat$param$rct.sympt.prob)
dat$attr$uCT.sympt[uCT == 1] <- rbinom(sum(uCT == 1), 1, dat$param$uct.sympt.prob)
dat$attr$rCT.infTime <- dat$attr$uCT.infTime <- rep(NA, num)
dat$attr$rCT.infTime[dat$attr$rCT == 1] <- 1
dat$attr$uCT.infTime[dat$attr$uCT == 1] <- 1
dat$attr$rCT.timesInf <- rep(0, num)
dat$attr$rCT.timesInf[rCT == 1] <- 1
dat$attr$uCT.timesInf <- rep(0, num)
dat$attr$uCT.timesInf[uCT == 1] <- 1
dat$attr$rCT.tx <- dat$attr$uCT.tx <- rep(NA, num)
dat$attr$rCT.tx.prep <- dat$attr$uCT.tx.prep <- rep(NA, num)
return(dat)
}
#' @title Re-Initialization Module
#'
#' @description This function reinitializes an epidemic model to restart at a
#' specified time step given an input \code{netsim} object.
#'
#' @param x An \code{EpiModel} object of class \code{\link{netsim}}.
#' @inheritParams initialize_msm
#'
#' @details
#' Currently, the necessary components that must be on \code{x} for a simulation
#' to be restarted must be: param, control, nwparam, epi, attr, temp, el, p.
#' TODO: describe this more.
#'
#' @return
#' This function resets the data elements on the \code{dat} master data object
#' in the needed ways for the time loop to function.
#'
#' @export
#' @keywords module msm
#'
reinit_msm <- function(x, param, init, control, s) {
need.for.reinit <- c("param", "control", "nwparam", "epi", "attr", "temp", "el", "p")
if (!all(need.for.reinit %in% names(x))) {
stop("x must contain the following elements for restarting: ",
"param, control, nwparam, epi, attr, temp, el, p",
call. = FALSE)
}
if (length(x$el) == 1) {
s <- 1
}
dat <- list()
dat$param <- param
dat$param$modes <- 1
dat$control <- control
dat$nwparam <- x$nwparam
dat$epi <- sapply(x$epi, function(var) var[s])
names(dat$epi) <- names(x$epi)
dat$el <- x$el[[s]]
dat$p <- x$p[[s]]
dat$attr <- x$attr[[s]]
if (!is.null(x$stats)) {
dat$stats <- list()
if (!is.null(x$stats$nwstats)) {
dat$stats$nwstats <- x$stats$nwstats[[s]]
}
}
dat$temp <- x$temp[[s]]
class(dat) <- "dat"
return(dat)
}
# HET -----------------------------------------------------------------
#' @export
#' @rdname initialize_msm
initialize_het <- function(x, param, init, control, s) {
dat <- list()
dat$temp <- list()
nw <- simulate(x$fit, control = control.simulate.ergm(MCMC.burnin = 1e6))
dat$el <- list()
dat$el[[1]] <- as.edgelist(nw)
attributes(dat$el)$vnames <- NULL
p <- tergmLite::stergm_prep(nw, x$formation, x$coef.diss$dissolution, x$coef.form,
x$coef.diss$coef.adj, x$constraints)
p$model.form$formula <- NULL
p$model.diss$formula <- NULL
dat$p <- list()
dat$p[[1]] <- p
## Network Model Parameters
dat$nwparam <- list(x[-which(names(x) == "fit")])
## Simulation Parameters
dat$param <- param
dat$param$modes <- 1
dat$init <- init
dat$control <- control
## Nodal Attributes
dat$attr <- list()
dat$attr$male <- get.vertex.attribute(nw, "male")
n <- network.size(nw)
dat$attr$active <- rep(1, n)
dat$attr$entTime <- rep(1, n)
dat <- initStatus_het(dat)
age <- rep(NA, n)
age[dat$attr$male == 0] <- sample(init$ages.feml, sum(dat$attr$male == 0), TRUE)
age[dat$attr$male == 1] <- sample(init$ages.male, sum(dat$attr$male == 1), TRUE)
dat$attr$age <- age
dat <- initInfTime_het(dat)
dat <- initDx_het(dat)
dat <- initTx_het(dat)
# Circumcision
male <- dat$attr$male
nMales <- sum(male == 1)
age <- dat$attr$age
circStat <- circTime <- rep(NA, n)
circStat[male == 1] <- rbinom(nMales, 1, dat$param$circ.prob.birth)
isCirc <- which(circStat == 1)
circTime[isCirc] <- round(-age[isCirc] * (365 / dat$param$time.unit))
dat$attr$circStat <- circStat
dat$attr$circTime <- circTime
## Stats List
dat$stats <- list()
## Final steps
dat$epi <- list()
dat <- prevalence_het(dat, at = 1)
}
#' @title Reinitialization Module
#'
#' @description This function reinitializes the master \code{dat} object on which
#' data are stored, simulates the initial state of the network, and
#' simulates disease status and other attributes.
#'
#' @param x An \code{EpiModel} object of class \code{\link{netest}}.
#' @param param An \code{EpiModel} object of class \code{\link{param_het}}.
#' @param init An \code{EpiModel} object of class \code{\link{init_het}}.
#' @param control An \code{EpiModel} object of class \code{\link{control_het}}.
#' @param s Simulation number, used for restarting dependent simulations.
#'
#' @return
#' This function returns the updated \code{dat} object with the initialized values
#' for demographics and disease-related variables.
#'
#' @keywords module het
#'
#' @export
#'
reinit_het <- function(x, param, init, control, s) {
need.for.reinit <- c("param", "control", "nwparam", "epi",
"attr", "temp", "el", "p")
if (!all(need.for.reinit %in% names(x))) {
stop("x must contain the following elements for restarting: ",
"param, control, nwparam, epi, attr, temp, el, p",
call. = FALSE)
}
if (length(x$el) == 1) {
s <- 1
}
dat <- list()
dat$param <- param
dat$param$modes <- 1
dat$control <- control
dat$nwparam <- x$nwparam
dat$epi <- sapply(x$epi, function(var) var[s])
names(dat$epi) <- names(x$epi)
dat$el <- x$el[[s]]
dat$p <- x$p[[s]]
dat$attr <- x$attr[[s]]
if (!is.null(x$stats)) {
dat$stats <- list()
if (!is.null(x$stats$nwstats)) {
dat$stats$nwstats <- x$stats$nwstats[[s]]
}
}
dat$temp <- x$temp[[s]]
class(dat) <- "dat"
return(dat)
}
initStatus_het <- function(dat) {
## Variables
i.prev.male <- dat$init$i.prev.male
i.prev.feml <- dat$init$i.prev.feml
male <- dat$attr$male
idsMale <- which(male == 1)
idsFeml <- which(male == 0)
nMale <- length(idsMale)
nFeml <- length(idsFeml)
n <- nMale + nFeml
## Process
status <- rep(0, n)
status[sample(idsMale, round(i.prev.male * nMale))] <- 1
status[sample(idsFeml, round(i.prev.feml * nFeml))] <- 1
dat$attr$status <- status
return(dat)
}
initInfTime_het <- function(dat) {
status <- dat$attr$status
n <- length(status)
infecteds <- which(status == 1)
infTime <- rep(NA, n)
inf.time.dist <- dat$init$inf.time.dist
if (inf.time.dist == "allacute") {
max.inf.time <- dat$param$vl.acute.topeak + dat$param$vl.acute.toset
infTime[infecteds] <- sample(0:(-max.inf.time), length(infecteds), TRUE)
} else {
max.inf.time <- dat$init$max.inf.time / dat$param$time.unit
if (inf.time.dist == "geometric") {
total.d.rate <- 1/max.inf.time
infTime[infecteds] <- -rgeom(length(infecteds), total.d.rate)
}
if (inf.time.dist == "uniform") {
infTime[infecteds] <- sample(0:(-max.inf.time), length(infecteds), TRUE)
}
}
## Enforce that time infected < age
infTime[infecteds] <- pmax(infTime[infecteds],
1 - dat$attr$age[infecteds] * (365 / dat$param$time.unit))
dat$attr$infTime <- infTime
timeInf <- 1 - infTime
dat$attr$ageInf <- pmax(0, dat$attr$age - round(timeInf) * (dat$param$time.unit / 365))
stopifnot(all(dat$attr$ageInf[infecteds] <= dat$attr$age[infecteds]),
all(dat$attr$ageInf[infecteds] >= 0))
return(dat)
}
initDx_het <- function(dat) {
n <- sum(dat$attr$active == 1)
status <- dat$attr$status
dxStat <- rep(NA, n)
dxStat[status == 1] <- 0
dxTime <- rep(NA, n)
dat$attr$dxStat <- dxStat
dat$attr$dxTime <- dxTime
return(dat)
}
initTx_het <- function(dat) {
## Variables
status <- dat$attr$status
n <- sum(dat$attr$active == 1)
nInf <- sum(status == 1)
tx.init.cd4.mean <- dat$param$tx.init.cd4.mean
tx.init.cd4.sd <- dat$param$tx.init.cd4.sd
tx.elig.cd4 <- dat$param$tx.elig.cd4
## Process
dat$attr$txStat <- rep(NA, n)
dat$attr$txStartTime <- rep(NA, n)
dat$attr$txStops <- rep(NA, n)
dat$attr$txTimeOn <- rep(NA, n)
dat$attr$txTimeOff <- rep(NA, n)
txCD4min <- rep(NA, n)
txCD4min[status == 1] <- pmin(rnbinom(nInf,
size = nbsdtosize(tx.init.cd4.mean,
tx.init.cd4.sd),
mu = tx.init.cd4.mean), tx.elig.cd4)
dat$attr$txCD4min <- txCD4min
dat$attr$txCD4start <- rep(NA, n)
dat$attr$txType <- rep(NA, n)
return(dat)
}
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