tempR/mod.prevalence.R

In dth2/EpiModelHIV_SHAMP: Epidemic Modeling of HIV Transmission among MSM, MSMF and Heterosexual Populations

#' @title Prevalence Calculations within Time Steps
#'
#' @description This module calculates demographic, transmission, and clinical
#'              statistics at each time step within the simulation.
#'
#' @inheritParams aging_msm
#'
#' @details
#' Summary statistic calculations are of two broad forms: prevalence and
#' incidence. This function establishes the summary statistic vectors for both
#' prevalence and incidence at time 1, and then calculates the prevalence
#' statistics for times 2 onward. Incidence statistics (e.g., number of new
#' infections or deaths) are calculated within the modules as they depend on
#' vectors that are not stored external to the module.
#'
#' @return
#' This function returns the \code{dat} object with an updated summary of current
#' attributes stored in \code{dat$epi}.
#'
#' @keywords module msm
#'
#' @export
#'
prevalence_msm <- function(dat, at) {

  race <- dat$attr$race
  status <- dat$attr$status
  prepStat <- dat$attr$prepStat

  nsteps <- dat$control$nsteps
  rNA <- rep(NA, nsteps)

  if (at == 1) {
    dat$epi$num <- rNA
    dat$epi$num.B <- rNA
    dat$epi$num.W <- rNA
    dat$epi$s.num <- rNA
    dat$epi$i.num <- rNA
    dat$epi$i.num.B <- rNA
    dat$epi$i.num.W <- rNA
    dat$epi$i.prev <- rNA
    dat$epi$i.prev.B <- rNA
    dat$epi$i.prev.W <- rNA
    dat$epi$nBirths <- rNA
    dat$epi$dth.gen <- rNA
    dat$epi$dth.dis <- rNA
    dat$epi$incid <- rNA

    dat$epi$prepCurr <- rNA
    dat$epi$prepCov <- rNA
    dat$epi$prepElig <- rNA
    dat$epi$prepStart <- rNA
    dat$epi$incid.prep0 <- rNA
    dat$epi$incid.prep1 <- rNA
    dat$epi$i.num.prep0 <- rNA
    dat$epi$i.num.prep1 <- rNA

    dat$epi$cprob.always.pers <- rNA
    dat$epi$cprob.always.inst <- rNA
  }


  dat$epi$num[at] <- length(status)
  dat$epi$num.B[at] <- sum(race == "B", na.rm = TRUE)
  dat$epi$num.W[at] <- sum(race == "W", na.rm = TRUE)
  dat$epi$s.num[at] <- sum(status == 0, na.rm = TRUE)
  dat$epi$i.num[at] <- sum(status == 1, na.rm = TRUE)
  dat$epi$i.num.B[at] <- sum(status == 1 & race == "B", na.rm = TRUE)
  dat$epi$i.num.W[at] <- sum(status == 1 & race == "W", na.rm = TRUE)
  dat$epi$i.prev[at] <- dat$epi$i.num[at] / dat$epi$num[at]
  dat$epi$i.prev.B[at] <- dat$epi$i.num.B[at] / dat$epi$num.B[at]
  dat$epi$i.prev.W[at] <- dat$epi$i.num.W[at] / dat$epi$num.W[at]

  dat$epi$prepCurr[at] <- sum(prepStat == 1, na.rm = TRUE)
  dat$epi$prepElig[at] <- sum(dat$attr$prepElig == 1, na.rm = TRUE)
  dat$epi$i.num.prep0[at] <- sum((is.na(prepStat) | prepStat == 0) &
                                 status == 1, na.rm = TRUE)
  dat$epi$i.num.prep1[at] <- sum(prepStat == 1 & status == 1, na.rm = TRUE)
  dat$epi$i.prev.prep0[at] <- dat$epi$i.num.prep0[at] /
                              sum((is.na(prepStat) | prepStat == 0), na.rm = TRUE)
  if (at == 1) {
    dat$epi$i.prev.prep1[1] <- 0
  } else {
    dat$epi$i.prev.prep1[at] <- dat$epi$i.num.prep1[at] /
                                sum(prepStat == 1, na.rm = TRUE)
  }

  return(dat)
}


#' @title Prevalence Module
#'
#' @description Module function to calculate and store summary statistics for
#'              disease prevalence, demographics, and other epidemiological
#'              outcomes.
#'
#' @inheritParams aging_het
#'
#' @keywords module het
#'
#' @export
#'
prevalence_het <- function(dat, at) {

  status <- dat$attr$status
  male <- dat$attr$male
  age <- dat$attr$age

  nsteps <- dat$control$nsteps
  rNA <- rep(NA, nsteps)

  # Initialize vectors
  if (at == 1) {
    dat$epi$i.num <- rNA
    dat$epi$num <- rNA

    dat$epi$i.num.male <- rNA
    dat$epi$i.num.feml <- rNA
    dat$epi$i.prev.male <- rNA
    dat$epi$i.prev.feml <- rNA

    dat$epi$num.male <- rNA
    dat$epi$num.feml <- rNA
    dat$epi$meanAge <- rNA
    dat$epi$propMale <- rNA

    dat$epi$si.flow <- rNA
    dat$epi$si.flow.male <- rNA
    dat$epi$si.flow.feml <- rNA

    dat$epi$b.flow <- rNA
    dat$epi$ds.flow <- dat$epi$di.flow <- rNA
  }

  dat$epi$i.num[at] <- sum(status == 1, na.rm = TRUE)
  dat$epi$num[at] <- length(status)

  dat$epi$i.num.male[at] <- sum(status == 1 & male == 1, na.rm = TRUE)
  dat$epi$i.num.feml[at] <- sum(status == 1 & male == 0, na.rm = TRUE)
  dat$epi$i.prev.male[at] <- sum(status == 1 & male == 1, na.rm = TRUE) /
    sum(male == 1, na.rm = TRUE)
  dat$epi$i.prev.feml[at] <- sum(status == 1 & male == 0, na.rm = TRUE) /
    sum(male == 0, na.rm = TRUE)

  dat$epi$num.male[at] <- sum(male == 1, na.rm = TRUE)
  dat$epi$num.feml[at] <- sum(male == 0, na.rm = TRUE)
  dat$epi$meanAge[at] <- mean(age, na.rm = TRUE)
  dat$epi$propMale[at] <- mean(male, na.rm = TRUE)

  return(dat)
}


whichVlSupp <- function(attr, param) {
  which(attr$status == 1 &
        attr$vlLevel <= log10(50) &
        (attr$age - attr$ageInf) * (365 / param$time.unit) >
        (param$vl.acute.topeak + param$vl.acute.toset))
}