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icm.mod.status.R
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icm.mod.status.R
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#' @title Primary Infection Module for icm
#'
#' @description This function simulates the main infection process given the
#' current state of the actors in the system.
#'
#' @inheritParams prevalence.icm
#'
#' @inherit recovery.icm return
#'
#' @export
#' @keywords internal
#'
infection.icm <- function(dat, at) {
## Expected acts
acts <- round(dat$param$act.rate * dat$epi$num[at - 1] / 2)
## Edgelist
p1 <- ssample(which(dat$attr$active == 1), acts, replace = TRUE)
p2 <- ssample(which(dat$attr$active == 1), acts, replace = TRUE)
del <- NULL
if (length(p1) > 0 && length(p2) > 0) {
del <- data.frame(p1, p2)
while (any(del$p1 == del$p2)) {
del$p2 <- ifelse(del$p1 == del$p2,
ssample(which(dat$attr$active == 1), 1), del$p2)
}
## Discordant edgelist
del$p1.stat <- dat$attr$status[del$p1]
del$p2.stat <- dat$attr$status[del$p2]
serodis <- (del$p1.stat == "s" & del$p2.stat == "i") |
(del$p1.stat == "i" & del$p2.stat == "s")
del <- del[serodis == TRUE, ]
## Transmission on edgelist
if (nrow(del) > 0) {
del$tprob <- dat$param$inf.prob
if (!is.null(dat$param$inter.eff) && at >= dat$param$inter.start) {
del$tprob <- del$tprob * (1 - dat$param$inter.eff)
}
del$trans <- rbinom(nrow(del), 1, del$tprob)
del <- del[del$trans == TRUE, ]
if (nrow(del) > 0) {
newIds <- unique(ifelse(del$p1.stat == "s", del$p1, del$p2))
nInf <- length(newIds)
dat$attr$status[newIds] <- "i"
dat$attr$infTime[newIds] <- at
} else {
nInf <- 0
}
} else {
nInf <- 0
}
} else {
nInf <- 0
}
## Output
if (at == 2) {
dat$epi$si.flow <- c(0, nInf)
} else {
dat$epi$si.flow[at] <- nInf
}
return(dat)
}
#' @title Recovery: icm Module
#'
#' @description This function simulates recovery from the infected state
#' either to a distinct recovered state (SIR model type) or back
#' to a susceptible state (SIS model type), for use in
#' \code{\link{icm}}.
#'
#' @inheritParams prevalence.icm
#'
#' @return The updated \code{icm_dat} class main data object.
#'
#' @export
#' @keywords internal
#'
recovery.icm <- function(dat, at) {
# Conditions --------------------------------------------------------------
if (!(dat$control$type %in% c("SIR", "SIS"))) {
return(dat)
}
# Variables ---------------------------------------------------------------
active <- dat$attr$active
status <- dat$attr$status
type <- dat$control$type
recovState <- ifelse(type == "SIR", "r", "s")
rec.rate <- dat$param$rec.rate
nRecov <- 0
idsElig <- which(active == 1 & status == "i")
nElig <- length(idsElig)
if (nElig > 0) {
vecRecov <- which(rbinom(nElig, 1, rec.rate) == 1)
if (length(vecRecov) > 0) {
idsRecov <- idsElig[vecRecov]
nRecov <- length(idsRecov)
status[idsRecov] <- recovState
}
}
dat$attr$status <- status
# Output ------------------------------------------------------------------
outName <- ifelse(type == "SIR", "ir.flow", "is.flow")
## Summary statistics
if (at == 2) {
dat$epi[[outName[1]]] <- c(0, nRecov)
} else {
dat$epi[[outName[1]]][at] <- nRecov
}
return(dat)
}
#' @title Primary Infection Module for icm
#'
#' @description This function simulates the main infection process given the
#' current state of the actors in the system.
#'
#' @inheritParams prevalence.icm
#'
#' @inherit recovery.icm return
#'
#' @export
#' @keywords internal
#'
infection.icm.bip <- function(dat, at) {
## Expected acts
if (dat$param$balance == "g1") {
acts <- round(dat$param$act.rate *
(dat$epi$num[at - 1] + dat$epi$num.g2[at - 1]) / 2)
}
if (dat$param$balance == "g2") {
acts <- round(dat$param$act.rate.g2 *
(dat$epi$num[at - 1] + dat$epi$num.g2[at - 1]) / 2)
}
## Edgelist
p1 <- ssample(which(dat$attr$active == 1 & dat$attr$group == 1),
acts, replace = TRUE)
p2 <- ssample(which(dat$attr$active == 1 & dat$attr$group == 2),
acts, replace = TRUE)
del <- NULL
if (length(p1) > 0 && length(p2) > 0) {
del <- data.frame(p1, p2)
## Discordant edgelist
del$p1.stat <- dat$attr$status[del$p1]
del$p2.stat <- dat$attr$status[del$p2]
serodis <- (del$p1.stat == "s" & del$p2.stat == "i") |
(del$p1.stat == "i" & del$p2.stat == "s")
del <- del[serodis == TRUE, ]
## Transmission on edgelist
if (nrow(del) > 0) {
del$tprob <- ifelse(del$p1.stat == "s", dat$param$inf.prob,
dat$param$inf.prob.g2)
if (!is.null(dat$param$inter.eff) && at >= dat$param$inter.start) {
del$tprob <- del$tprob * (1 - dat$param$inter.eff)
}
del$trans <- rbinom(nrow(del), 1, del$tprob)
del <- del[del$trans == TRUE, ]
if (nrow(del) > 0) {
newIdsg1 <- unique(del$p1[del$p1.stat == "s"])
newIdsg2 <- unique(del$p2[del$p2.stat == "s"])
nInf <- length(newIdsg1)
nInfg2 <- length(newIdsg2)
newIds <- c(newIdsg1, newIdsg2)
dat$attr$status[newIds] <- "i"
dat$attr$infTime[newIds] <- at
} else {
nInf <- nInfg2 <- 0
}
} else {
nInf <- nInfg2 <- 0
}
} else {
nInf <- nInfg2 <- 0
}
## Output
if (at == 2) {
dat$epi$si.flow <- c(0, nInf)
dat$epi$si.flow.g2 <- c(0, nInfg2)
} else {
dat$epi$si.flow[at] <- nInf
dat$epi$si.flow.g2[at] <- nInfg2
}
return(dat)
}
#' @title Recovery: icm Module
#'
#' @description This function simulates recovery from the infected state
#' either to a distinct recovered state (SIR model type) or back
#' to a susceptible state (SIS model type), for use in
#' \code{\link{icm}}.
#'
#' @inheritParams prevalence.icm
#'
#' @inherit recovery.icm return
#'
#' @export
#' @keywords internal
#'
recovery.icm.bip <- function(dat, at) {
# Conditions --------------------------------------------------------------
if (!(dat$control$type %in% c("SIR", "SIS"))) {
return(dat)
}
# Variables ---------------------------------------------------------------
active <- dat$attr$active
status <- dat$attr$status
group <- dat$attr$group
type <- dat$control$type
recovState <- ifelse(type == "SIR", "r", "s")
rec.rate <- dat$param$rec.rate
rec.rate.g2 <- dat$param$rec.rate.g2
nRecov <- nRecovG2 <- 0
idsElig <- which(active == 1 & status == "i")
nElig <- length(idsElig)
if (nElig > 0) {
gElig <- group[idsElig]
rates <- c(rec.rate, rec.rate.g2)
ratesElig <- rates[gElig]
vecRecov <- which(rbinom(nElig, 1, ratesElig) == 1)
if (length(vecRecov) > 0) {
idsRecov <- idsElig[vecRecov]
nRecov <- sum(group[idsRecov] == 1)
nRecovG2 <- sum(group[idsRecov] == 2)
status[idsRecov] <- recovState
}
}
dat$attr$status <- status
# Output ------------------------------------------------------------------
outName <- ifelse(type == "SIR", "ir.flow", "is.flow")
outName[2] <- paste0(outName, ".g2")
## Summary statistics
if (at == 2) {
dat$epi[[outName[1]]] <- c(0, nRecov)
} else {
dat$epi[[outName[1]]][at] <- nRecov
}
if (at == 2) {
dat$epi[[outName[2]]] <- c(0, nRecovG2)
} else {
dat$epi[[outName[2]]][at] <- nRecovG2
}
return(dat)
}