R/params.shamp.R
In dth2/EpiModelHIV_SHAMP: Epidemic Modeling of HIV Transmission among MSM, MSMF and Heterosexual Populations
# SHAMP -----------------------------------------------------------------
#' @title Epidemic Model Parameters for SHAMP
#'
#' @description Sets the epidemic parameters for stochastic network models
#' simulated with \code{\link{netsim}} for EpiModelHIV
#'
#' @param last.neg.test.R.S.int Time range in days for last negative test for
#' race group R where R is a charater (B, BI, H, HI or W) and sex group S
#' where s is a charter (f, msf, msm, msmf).
#' @param mean.test.B.int Mean intertest interval in days for race group R where R
#' is a charater (B, BI, H, HI or W) and sex group S
#' where s is a charter (f, msf, msm, msmf) who test.
#' @param testing.pattern Method for HIV testing, with options \code{"memoryless"}
#' for constant hazard without regard to time since previous test, or
#' \code{"interval"} deterministic fixed intervals.
#' @param test.window.int Length of the HIV test window period in days.
#' @param tt.traj.R.S.prob Proportion of race group R, where R is a charater (B, BI, H, HI or W),
#' and sex group S, where s is a charter (f, msf, msm, msmf), who enter one of four
#' testing/treatment trajectories: never test or treat, test and never
#' initiate treatment, test and treated with partial viral suppression,
#' and test and treated with full suppression.
#' @param tx.init.R.S.prob Probability per time step that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' who has tested positive will initiate treatment.
#' @param tx.halt.R.S.prob Probability per time step that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' who is currently on treatment will halt treatment.
#' @param tx.reinit.R.S.prob Probability per time step that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' who is not currently on treatment but who has been in the past will
#' re-initiate treatment.
#' @param max.time.off.tx.full.int Number of days off treatment for a full
#' suppressor before onset of AIDS, including time before diagnosis.
#' @param max.time.on.tx.part.int Number of days on treatment for a
#' partial suppressor beofre onset of AIDS.
#' @param max.time.off.tx.part.int Nnumber of days off treatment for a
#' partial suppressor before onset of AIDS, including time before
#' diagnosis.
#' @param vl.acute.rise.int Number of days to peak viremia during acute
#' infection.
#' @param vl.acute.peak Peak viral load (in log10 units) at the height of acute
#' infection.
#' @param vl.acute.fall.int Number of days from peak viremia to set-point
#' viral load during the acute infection period.
#' @param vl.set.point Set point viral load (in log10 units).
#' @param vl.aids.onset.int Number of days to AIDS for a treatment-naive
#' patient.
#' @param vl.aids.int Duration of AIDS stage infection in days.
#' @param vl.fatal Viral load in AIDS at which death occurs.
#' @param vl.full.supp Log10 viral load at full suppression on ART.
#' @param vl.part.supp Log10 viral load at partial suppression on ART.
#' @param full.supp.down.slope For full suppressors, number of log10 units that
#' viral load falls per time step from treatment initiation or re-initiation
#' until the level in \code{vl.full.supp}.
#' @param full.supp.up.slope For full suppressors, number of log10 units that
#' viral load rises per time step from treatment halting until expected
#' value.
#' @param part.supp.down.slope For partial suppressors, number of log10 units
#' that viral load falls per time step from treatment initiation or
#' re-initiation until the level in \code{vl.part.supp}.
#' @param part.supp.up.slope For partial suppressors, number of log10 units that
#' viral load rises per time step from treatment halting until expected value.
#' @param b.R.S.rate Rate at which a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' enters the population.
#' @param birth.age Age (in years) of new arrivals.
#' @param b.method Method for calculating the number of expected births at each
#' time step, with \code{"fixed"} based on the number of persons at the
#' initial time step and \code{"varying"} based on the current time step.
#' @param age.adj This value is added to the squareroot of age for females in sqrt.age.adj
#' so that sex age asymmetry can be captured with an absdiff term
#' @param msm.frac The fraction of males entering the population at each time step
#' as a male that has intercourse exclusivly with other males "msm" -
#' males not designated as msm or msmf are considered "msf".
#' @param msmf.frac The fraction of males entering the population at each time step as
#' as a male that has intercourse with both males and females "msmf" -
#' males not designated as msm or msmf are considered "msf".
#' @param URAI.prob Probability of transmission for a man having unprotected
#' receptive anal intercourse with an infected man at set point viral
#' load.
#' @param UIAI.prob Probability of transmission for an uncircumcised man having
#' unprotected insertive anal intercourse with an infected man at set
#' point viral load.
#' @param URVI.prob Probability of transmission for a woman having unprotected
#' receptive vaginal intercourse with an infected man at set point viral
#' load.
#' @param UIVI.prob Probability of transmission for an uncircumcised man having
#' unprotected insertive vaginal intercourse with an infected women at set
#' point viral load.
#' @param acute.rr Relative risk of infection (compared to that predicted by
#' elevated viral load) when positive partner is in the acute stage.
#' @param circ.rr Relative risk of infection from insertive anal sex when the
#' negative insertive partner is circumcised.
#' @param condom.rr Relative risk of infection from anal sex when a condom is
#' used.
#' @param disc.outset.main.R.S.prob Probability that an HIV-infected person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), will disclose their status at the start of a main partnership.
#' @param disc.at.diag.main.R.S.prob Probability that a a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' already in a main partnership will disclose at the time of diagnosis.
#' @param disc.post.diag.main.R.S.prob Probability that an HIV-infected a person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), in a main partnership will disclose their status, assuming they didn't
#' at the start of the partnership or at diagnosis.
#' @param disc.outset.pers.R.S.prob Probability that an HIV-infected a person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), will disclose their status at the start of a casual partnership.
#' @param disc.at.diag.pers.R.S.prob Probability that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' already in a casual partnership will disclose at the time of diagnosis.
#' @param disc.post.diag.pers.R.S.prob Probability that an HIV-infected person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), in a casual partnership will disclose their status, assuming they
#' didn't at the start of the partnership or at diagnosis.
#' @param disc.inst.R.S.prob Probability that an HIV-infected person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), will disclose ther status to a one-off partner.
#' @param circ.R.prob Probablity that a male in race group R, where R is a
#' charater (B, BI, H, HI or W), newly arriving in the population
#' will be circumcised.
#' @param ccr5.R.S.prob Vector of length two of frequencies of the Delta 32
#' mutation (homozygous and heterozygous, respectively) in the CCR5 gene
#' among people in race group R, where R is a charater (B, BI, H, HI or W),
#' and sex group S, where s is a charter (f or m).
#' @param ccr5.heteroz.rr Relative risk of infection for people who are heterozygous
#' in the CCR5 mutation.
#' @param num.inst.ai.classes Number of quantiles into which men should be
#' divided in determining their levels of one-off anal intercourse.
#' @param base.ai.main.R.rate Expected coital frequency (for anal intercourse) for males
#' (msm and msmf) in race group R, where R is a charater (B, BI, H, HI or W),
#' in main partnerships (acts per day).
#' @param base.vi.main.R.rate Expected coital frequency (for vaginal intercourse) for
#' males and females in race group R, where R is a charater (B, BI, H, HI or W),
#' in main partnerships (acts per day).
#' @param base.ai.pers.R.rate Expected coital frequency (for anal intercourse) for males
#' (msm and msmf) in race group R, where R is a charater (B, BI, H, HI or W),
#' in casual partnerships (acts per day).
#' @param base.vi.pers.R.rate Expected coital frequency (for vaginal intercourse) for
#' males and females in race group R, where R is a charater (B, BI, H, HI or W),
#' in casual partnerships (acts per day).
#' @param ai.scale General relative scaler for all ai act rates for model
#' calibration.
#' @param vi.scale General relative scaler for all vi act rates for model
#' calibration.
#' @param cond.main.R.prob.msm Probability of condom use by an male of race group R,
#' where R is a charater (B, BI, H, HI or W), in a male-male main partnership.
#' @param cond.pers.always.prob.msm Fraction of men in male-male casual partnerships who always
#' use condoms in those partnerships.
#' @param cond.pers.R.prob.msm Of men who are not consistent condom users, per-act
#' probability of condom use in a casual male-male partnership by
#' males of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.inst.always.prob.msm Fraction of men in male-male one-time partnerships who always
#' use condoms in those partnerships.
#' @param cond.inst.R.prob.msm Of men who are not consistent condom users, per-act
#' probability of condom use in a one-time male-male partnership by
#' males of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.always.prob.corr.msm Correlation coefficient for probability of always
#' using condoms in both casual and one-off male-male partnerships
#' @param cond.main.R.prob.het Probability of condom use by an person of race group R,
#' where R is a charater (B, BI, H, HI or W), in a heterosexual main partnership.
#' @param cond.pers.always.prob.het Fraction of people in heterosexual casual partnerships who always
#' use condoms in those partnerships.
#' @param cond.pers.R.prob.het Of people who are not consistent condom users, per-act
#' probability of condom use in a casual heterosexual partnership by
#' people of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.inst.always.prob.het Fraction of people in heterosexual one-time partnerships who always
#' use condoms in those partnerships.
#' @param cond.inst.R.prob.het Of people who are not consistent condom users, per-act
#' probability of condom use in a one-time heterosexual partnership by
#' males of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.always.prob.corr.het Correlation coefficient for probability of always
#' using condoms in both casual and one-off heterosexual partnerships
#' @param cond.diag.main.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male main partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.main.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male main partnership if the HIV-infected man has disclosed.
#' @param cond.diag.pers.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male casual partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.pers.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male casual partnership if the HIV-infected man has disclosed
#' his status.
#' @param cond.diag.inst.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male one-off partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.inst.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male one-off partnership if the HIV-infected man has disclosed
#' his status.
#' @param cond.diag.main.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual main partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.main.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual main partnership if the HIV-infected man has disclosed.
#' @param cond.diag.pers.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual casual partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.pers.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual casual partnership if the HIV-infected man has disclosed
#' his status.
#' @param cond.diag.inst.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual one-off partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.inst.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual one-off partnership if the HIV-infected man has disclosed
#' his status.
#' @param vv.iev.R.prob Probability that a person of group R, where R is a charater (B, BI, H, HI or W),
#' in a male male relationship will engage in intra-event versatility
#' ("flipping") given that they're having AI.
#' @param prep.start Time step at which the PrEP intervention should start.
#' @param prep.elig.model Modeling approach for determining who is eligible for
#' PrEP. Current options are limited to: \code{"all"} for all persons who
#' have never been on PrEP and are disease-susceptible.
#' @param prep.class.prob The probability of adherence class in non-adherent,
#' low adherence, medium adherence, or high adherence groups (from Liu).
#' @param prep.class.hr The hazard ratio for infection per act associated with each
#' level of adherence (from Grant).
#' @param prep.coverage The proportion of the eligible population who are start
#' PrEP once they become eligible.
#' @param prep.cov.method The method for calculating PrEP coverage, with options
#' of \code{"curr"} to base the numerator on the number of people currently
#' on PrEP and \code{"ever"} to base it on the number of people ever on
#' PrEP.
#' @param prep.cov.rate The rate at which persons initiate PrEP conditional on
#' their eligibility, with 1 equal to instant start.
#' @param prep.tst.int Testing interval for those who are actively on PrEP. This
#' overrides the mean testing interval parameters.
#' @param prep.risk.int Time window for assessment of risk eligibility for PrEP
#' in days.
#' @param prep.risk.reassess If \code{TRUE}, reassess eligibility for PrEP at
#' each testing visit.
#'
#' @param immig.depart.R.S The probability per time step that a person of group R,
#' where R is a charater (BI or HI), and sex S, were S is a charater (f or m), leaves the population
#' being simulated by returning to their home country.
#' @param immig.return.R.S The probability per time step that a person of group R,
#' where R is a charater (BI or HI), and sex S, were S is a charater (f or m), who has left
#' the population being simulated by returning to their home country returns to the population
#' being simulated.
#' @param immig.aq.prob.R.S The probability per time step that a person of group R,
#' where R is a charater (BI or HI), and sex S, were S is a charater (f or m), who has left
#' the population being simulated aquires HIV while in their home country.
#'
#'
#'
#' @param ... Additional arguments passed to the function.
#'
#' @return
#' A list object of class \code{param_shamp}, which can be passed to
#' EpiModel function \code{netsim}.
#'
#' @keywords shamp
#'
#' @export
param_shamp <- function(race.method = 1,
time.unit = 7,
method=1,
last.neg.test.B.f.int = 1464,
last.neg.test.BI.f.int = 1464,
last.neg.test.H.f.int = 1464,
last.neg.test.HI.f.int = 1464,
last.neg.test.W.f.int = 1464,
last.neg.test.B.msf.int = 1708,
last.neg.test.BI.msf.int = 1708,
last.neg.test.H.msf.int = 1708,
last.neg.test.HI.msf.int = 1708,
last.neg.test.W.msf.int = 1708,
last.neg.test.B.msm.int = 1037,
last.neg.test.BI.msm.int = 1037,
last.neg.test.H.msm.int = 1037,
last.neg.test.HI.msm.int = 1037,
last.neg.test.W.msm.int = 1037,
last.neg.test.B.msmf.int = 1129,
last.neg.test.BI.msmf.int = 1129,
last.neg.test.H.msmf.int = 1129,
last.neg.test.HI.msmf.int = 1129,
last.neg.test.W.msmf.int = 1129,
mean.test.B.f.int = 1464,
mean.test.BI.f.int = 1464,
mean.test.H.f.int = 1464,
mean.test.HI.f.int = 1464,
mean.test.W.f.int = 1464,
mean.test.B.msf.int = 1708,
mean.test.BI.msf.int = 1708,
mean.test.H.msf.int = 1708,
mean.test.HI.msf.int = 1708,
mean.test.W.msf.int = 1708,
mean.test.B.msm.int = 1037,
mean.test.BI.msm.int = 1037,
mean.test.H.msm.int = 1037,
mean.test.HI.msm.int = 1037,
mean.test.W.msm.int = 1037,
mean.test.B.msmf.int = 1129,
mean.test.BI.msmf.int = 1129,
mean.test.H.msmf.int = 1129,
mean.test.HI.msmf.int = 1129,
mean.test.W.msmf.int = 1129,
testing.pattern = "memoryless",
test.window.int = 15,
tt.traj.B.f.prob = c(0.741, 0.000, 0.053, 0.206),
tt.traj.BI.f.prob = c(0.741, 0.000, 0.053, 0.206),
tt.traj.H.f.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.HI.f.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.W.f.prob = c(0.741, 0.000, 0.057, 0.202),
tt.traj.B.msf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.BI.msf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.H.msf.prob = c(0.741, 0.000, 0.067, 0.192),
tt.traj.HI.msf.prob = c(0.741, 0.000, 0.067, 0.192),
tt.traj.W.msf.prob = c(0.741, 0.000, 0.052, 0.207),
tt.traj.B.msm.prob = c(.025, 0, .065, .911),
tt.traj.BI.msm.prob = c(.025, 0, .065, .911),
tt.traj.H.msm.prob = c(.025, 0, .065, .911),
tt.traj.HI.msm.prob = c(.025, 0, .065, .911),
tt.traj.W.msm.prob = c(.025, 0, .065, .911),
tt.traj.B.msmf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.BI.msmf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.H.msmf.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.HI.msmf.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.W.msmf.prob = c(0.741, 0.000, 0.052, 0.207),
tx.init.B.f.prob = 0.2331,
tx.init.BI.f.prob = 0.2331,
tx.init.H.f.prob = 0.2331,
tx.init.HI.f.prob = 0.2331,
tx.init.W.f.prob = 0.2331,
tx.init.B.msf.prob = 0.2331,
tx.init.BI.msf.prob = 0.2331,
tx.init.H.msf.prob = 0.2331,
tx.init.HI.msf.prob = 0.2331,
tx.init.W.msf.prob = 0.2331,
tx.init.B.msm.prob = 0.1467,
tx.init.BI.msm.prob = 0.1467,
tx.init.H.msm.prob = 0.1467,
tx.init.HI.msm.prob = 0.1467,
tx.init.W.msm.prob = 0.1467,
tx.init.B.msmf.prob = 0.2331,
tx.init.BI.msmf.prob = 0.2331,
tx.init.H.msmf.prob = 0.2331,
tx.init.HI.msmf.prob = 0.2331,
tx.init.W.msmf.prob = 0.2331,
#No halting in the basic SHAMP model
tx.halt.B.f.prob = 0.0001,
tx.halt.BI.f.prob = 0.0001,
tx.halt.H.f.prob = 0.0001,
tx.halt.HI.f.prob = 0.0001,
tx.halt.W.f.prob = 0.0001,
tx.halt.B.msf.prob = 0.0001,
tx.halt.BI.msf.prob = 0.0001,
tx.halt.H.msf.prob = 0.0001,
tx.halt.HI.msf.prob = 0.0001,
tx.halt.W.msf.prob = 0.0001,
tx.halt.B.msm.prob = 0.0001,
tx.halt.BI.msm.prob = 0.0001,
tx.halt.H.msm.prob = 0.0001,
tx.halt.HI.msm.prob = 0.0001,
tx.halt.W.msm.prob = 0.0001,
tx.halt.B.msmf.prob = 0.0001,
tx.halt.BI.msmf.prob = 0.0001,
tx.halt.H.msmf.prob = 0.0001,
tx.halt.HI.msmf.prob = 0.0001,
tx.halt.W.msmf.prob = 0.0001,
##With no halting there is also no re-initiation
tx.reinit.B.f.prob = 1,
tx.reinit.BI.f.prob = 1,
tx.reinit.H.f.prob = 1,
tx.reinit.HI.f.prob = 1,
tx.reinit.W.f.prob = 1,
tx.reinit.B.msf.prob = 1,
tx.reinit.BI.msf.prob = 1,
tx.reinit.H.msf.prob = 1,
tx.reinit.HI.msf.prob = 1,
tx.reinit.W.msf.prob = 1,
tx.reinit.B.msm.prob = 1,
tx.reinit.BI.msm.prob = 1,
tx.reinit.H.msm.prob = 1,
tx.reinit.HI.msm.prob = 1,
tx.reinit.W.msm.prob = 1,
tx.reinit.B.msmf.prob = 1,
tx.reinit.BI.msmf.prob = 1,
tx.reinit.H.msmf.prob = 1,
tx.reinit.HI.msmf.prob = 1,
tx.reinit.W.msmf.prob = 1,
max.time.off.tx.full.int = 520 * 7,
max.time.on.tx.part.int = 52 * 15 * 7,
max.time.off.tx.part.int = 520 * 7,
vl.acute.rise.int = 45,
vl.acute.peak = 6.886,
vl.acute.fall.int = 21,
vl.set.point = 4.5,
vl.aids.onset.int = 520 * 7,
vl.aids.int = 52 * 2 * 7,
vl.fatal = 7,
vl.full.supp = 1.5,
vl.part.supp = 3.5,
full.supp.down.slope = 0.25,
full.supp.up.slope = 0.25,
part.supp.down.slope = 0.25,
part.supp.up.slope = 0.25,
#Births are set for consistent population demographics.
#b.B.f.rate = 1e-3 / 7,
#b.BI.f.rate = 1e-3 / 7,
#b.H.f.rate = 1e-3 / 7,
#b.HI.f.rate = 1e-3 / 7,
#b.W.f.rate = 1e-3 / 7,
#b.B.m.rate = 1e-3 / 7,
#b.BI.m.rate = 1e-3 / 7,
#b.H.m.rate = 1e-3 / 7,
#b.HI.m.rate = 1e-3 / 7,
#b.W.m.rate = 1e-3 / 7,
birth.age = 18,
exit.age = 60,
b.method = "fixed",
msm.frac=0.0,
msmf.frac=0.012,
URAI.prob = 138/10000,
UIAI.prob = 11/10000,
URVI.prob = 8/10000,
UIVI.prob = 4/10000,
acute.rr = 4,
circ.rr = .4,
condom.rr = 0.25,
##For now aasume no disclosure (may use for calibration)
disc.outset.main.B.f.prob = 0,
disc.outset.main.BI.f.prob = 0,
disc.outset.main.H.f.prob = 0,
disc.outset.main.HI.f.prob = 0,
disc.outset.main.W.f.prob = 0,
disc.outset.main.B.msf.prob = 0,
disc.outset.main.BI.msf.prob = 0,
disc.outset.main.H.msf.prob = 0,
disc.outset.main.HI.msf.prob = 0,
disc.outset.main.W.msf.prob = 0,
disc.outset.main.B.msm.prob = 0,
disc.outset.main.BI.msm.prob = 0,
disc.outset.main.H.msm.prob = 0,
disc.outset.main.HI.msm.prob = 0,
disc.outset.main.W.msm.prob = 0,
disc.outset.main.B.msmf.prob = 0,
disc.outset.main.BI.msmf.prob = 0,
disc.outset.main.H.msmf.prob = 0,
disc.outset.main.HI.msmf.prob = 0,
disc.outset.main.W.msmf.prob = 0,
disc.at.diag.main.B.f.prob = 0,
disc.at.diag.main.BI.f.prob = 0,
disc.at.diag.main.H.f.prob = 0,
disc.at.diag.main.HI.f.prob = 0,
disc.at.diag.main.W.f.prob = 0,
disc.at.diag.main.B.msf.prob = 0,
disc.at.diag.main.BI.msf.prob = 0,
disc.at.diag.main.H.msf.prob = 0,
disc.at.diag.main.HI.msf.prob = 0,
disc.at.diag.main.W.msf.prob = 0,
disc.at.diag.main.B.msm.prob = 0,
disc.at.diag.main.BI.msm.prob = 0,
disc.at.diag.main.H.msm.prob = 0,
disc.at.diag.main.HI.msm.prob = 0,
disc.at.diag.main.W.msm.prob = 0,
disc.at.diag.main.B.msmf.prob = 0,
disc.at.diag.main.BI.msmf.prob = 0,
disc.at.diag.main.H.msmf.prob = 0,
disc.at.diag.main.HI.msmf.prob = 0,
disc.at.diag.main.W.msmf.prob = 0,
disc.post.diag.main.B.f.prob = 0,
disc.post.diag.main.BI.f.prob = 0,
disc.post.diag.main.H.f.prob = 0,
disc.post.diag.main.HI.f.prob = 0,
disc.post.diag.main.W.f.prob = 0,
disc.post.diag.main.B.msf.prob = 0,
disc.post.diag.main.BI.msf.prob = 0,
disc.post.diag.main.H.msf.prob = 0,
disc.post.diag.main.HI.msf.prob = 0,
disc.post.diag.main.W.msf.prob = 0,
disc.post.diag.main.B.msm.prob = 0,
disc.post.diag.main.BI.msm.prob = 0,
disc.post.diag.main.H.msm.prob = 0,
disc.post.diag.main.HI.msm.prob = 0,
disc.post.diag.main.W.msm.prob = 0,
disc.post.diag.main.B.msmf.prob = 0,
disc.post.diag.main.BI.msmf.prob = 0,
disc.post.diag.main.H.msmf.prob = 0,
disc.post.diag.main.HI.msmf.prob = 0,
disc.post.diag.main.W.msmf.prob = 0,
disc.outset.pers.B.f.prob = 0,
disc.outset.pers.BI.f.prob = 0,
disc.outset.pers.H.f.prob = 0,
disc.outset.pers.HI.f.prob = 0,
disc.outset.pers.W.f.prob = 0,
disc.outset.pers.B.msf.prob = 0,
disc.outset.pers.BI.msf.prob = 0,
disc.outset.pers.H.msf.prob = 0,
disc.outset.pers.HI.msf.prob = 0,
disc.outset.pers.W.msf.prob = 0,
disc.outset.pers.B.msm.prob = 0,
disc.outset.pers.BI.msm.prob = 0,
disc.outset.pers.H.msm.prob = 0,
disc.outset.pers.HI.msm.prob = 0,
disc.outset.pers.W.msm.prob = 0,
disc.outset.pers.B.msmf.prob = 0,
disc.outset.pers.BI.msmf.prob = 0,
disc.outset.pers.H.msmf.prob = 0,
disc.outset.pers.HI.msmf.prob = 0,
disc.outset.pers.W.msmf.prob = 0,
disc.at.diag.pers.B.f.prob = 0,
disc.at.diag.pers.BI.f.prob = 0,
disc.at.diag.pers.H.f.prob = 0,
disc.at.diag.pers.HI.f.prob = 0,
disc.at.diag.pers.W.f.prob = 0,
disc.at.diag.pers.B.msf.prob = 0,
disc.at.diag.pers.BI.msf.prob = 0,
disc.at.diag.pers.H.msf.prob = 0,
disc.at.diag.pers.HI.msf.prob = 0,
disc.at.diag.pers.W.msf.prob = 0,
disc.at.diag.pers.B.msm.prob = 0,
disc.at.diag.pers.BI.msm.prob = 0,
disc.at.diag.pers.H.msm.prob = 0,
disc.at.diag.pers.HI.msm.prob = 0,
disc.at.diag.pers.W.msm.prob = 0,
disc.at.diag.pers.B.msmf.prob = 0,
disc.at.diag.pers.BI.msmf.prob = 0,
disc.at.diag.pers.H.msmf.prob = 0,
disc.at.diag.pers.HI.msmf.prob = 0,
disc.at.diag.pers.W.msmf.prob = 0,
disc.post.diag.pers.B.f.prob = 0,
disc.post.diag.pers.BI.f.prob = 0,
disc.post.diag.pers.H.f.prob = 0,
disc.post.diag.pers.HI.f.prob = 0,
disc.post.diag.pers.W.f.prob = 0,
disc.post.diag.pers.B.msf.prob = 0,
disc.post.diag.pers.BI.msf.prob = 0,
disc.post.diag.pers.H.msf.prob = 0,
disc.post.diag.pers.HI.msf.prob = 0,
disc.post.diag.pers.W.msf.prob = 0,
disc.post.diag.pers.B.msm.prob = 0,
disc.post.diag.pers.BI.msm.prob = 0,
disc.post.diag.pers.H.msm.prob = 0,
disc.post.diag.pers.HI.msm.prob = 0,
disc.post.diag.pers.W.msm.prob = 0,
disc.post.diag.pers.B.msmf.prob = 0,
disc.post.diag.pers.BI.msmf.prob = 0,
disc.post.diag.pers.H.msmf.prob = 0,
disc.post.diag.pers.HI.msmf.prob = 0,
disc.post.diag.pers.W.msmf.prob = 0,
disc.inst.B.f.prob = 0,
disc.inst.BI.f.prob = 0,
disc.inst.H.f.prob = 0,
disc.inst.HI.f.prob = 0,
disc.inst.W.f.prob = 0,
disc.inst.B.msf.prob = 0,
disc.inst.BI.msf.prob = 0,
disc.inst.H.msf.prob = 0,
disc.inst.HI.msf.prob = 0,
disc.inst.W.msf.prob = 0,
disc.inst.B.msm.prob = 0,
disc.inst.BI.msm.prob = 0,
disc.inst.H.msm.prob = 0,
disc.inst.HI.msm.prob = 0,
disc.inst.W.msm.prob = 0,
disc.inst.B.msmf.prob = 0,
disc.inst.BI.msmf.prob = 0,
disc.inst.H.msmf.prob = 0,
disc.inst.HI.msmf.prob = 0,
disc.inst.W.msmf.prob = 0,
circ.B.prob = 0.770,
circ.BI.prob = 0.450,
circ.H.prob = 0.480,
circ.HI.prob = 0.360,
circ.W.prob = 0.910,
ccr5.B.f.prob = c(0, 0.0),
ccr5.BI.f.prob = c(0, 0.0),
ccr5.H.f.prob = c(0, 0.0),
ccr5.HI.f.prob = c(0, 0.0),
ccr5.W.f.prob = c(0.017, 0.0),
ccr5.B.m.prob = c(0, 0.0),
ccr5.BI.m.prob = c(0, 0.0),
ccr5.H.m.prob = c(0, 0.0),
ccr5.HI.m.prob = c(0, 0.0),
ccr5.W.m.prob = c(0.017, 0.0),
ccr5.heteroz.rr = 0,
num.inst.ai.classes = 1,
#not modeling msm but will include home testing SEattle estimates as placeholders
base.ai.main.B.rate = 1.379/7,
base.ai.main.BI.rate = 1.379/7,
base.ai.main.H.rate = 1.379/7,
base.ai.main.HI.rate = 1.379/7,
base.ai.main.W.rate = 1.379/7,
base.ai.pers.B.rate = 0.883/7,
base.ai.pers.BI.rate = 0.883/7,
base.ai.pers.H.rate = 0.883/7,
base.ai.pers.HI.rate = 0.883/7,
base.ai.pers.W.rate = 0.883/7,
base.vi.main.B.rate = 0.25 ,
base.vi.main.BI.rate = 0.19 ,
base.vi.main.H.rate = 0.26,
base.vi.main.HI.rate = 0.24,
base.vi.main.W.rate = 0.22,
base.vi.pers.B.rate = 0.12,
base.vi.pers.BI.rate = 0.09,
base.vi.pers.H.rate = 0.12,
base.vi.pers.HI.rate = 0.09,
base.vi.pers.W.rate = 0.12,
ai.scale = 1,
vi.scale = 1,
# For MSM pulled data from the mobile study but we are not modeling MSM for now.
cond.main.B.prob.msm = 0.062,
cond.main.BI.prob.msm = 0.062,
cond.main.H.prob.msm = 0.062,
cond.main.HI.prob.msm = 0.062,
cond.main.W.prob.msm = 0.062,
cond.pers.always.prob.msm = 0.01,
cond.pers.B.prob.msm = 0.0304,
cond.pers.BI.prob.msm = 0.0304,
cond.pers.H.prob.msm = 0.0304,
cond.pers.HI.prob.msm = 0.0304,
cond.pers.W.prob.msm = 0.0304,
cond.inst.always.prob.msm = 0.01,
cond.inst.B.prob.msm = 0.413,
cond.inst.BI.prob.msm = 0.413 ,
cond.inst.H.prob.msm = 0.413 ,
cond.inst.HI.prob.msm = 0.413 ,
cond.inst.W.prob.msm = 0.413 ,
cond.always.prob.corr.msm = 0.01,
cond.main.B.prob.het = 0.12,
cond.main.BI.prob.het = 0.17,
cond.main.H.prob.het = 0.17,
cond.main.HI.prob.het = 0.19,
cond.main.W.prob.het = 0.16,
cond.pers.always.prob.het = .0001,
cond.pers.B.prob.het = 0.54,
cond.pers.BI.prob.het = 0.65,
cond.pers.H.prob.het = 0.5,
cond.pers.HI.prob.het = 0.49,
cond.pers.W.prob.het = 0.48,
cond.inst.always.prob.het = .0001,
cond.inst.B.prob.het = 0.85,
cond.inst.BI.prob.het = 0.73,
cond.inst.H.prob.het = 0.78,
cond.inst.HI.prob.het = 0.76,
cond.inst.W.prob.het = 0.72,
cond.always.prob.corr.het = .0001,
#WAITING ON APRIL AS POSIBLE DATA SOURCE.
#Currect placeholder from home testing project
cond.diag.main.beta.msm = -0.67,
cond.discl.main.beta.msm = -0.85,
cond.diag.pers.beta.msm = -0.67,
cond.discl.pers.beta.msm = -0.85,
cond.diag.inst.beta.msm = -0.67,
cond.discl.inst.beta.msm = -0.85,
cond.diag.main.beta.het = 0,
cond.discl.main.beta.het = 0,
cond.diag.pers.beta.het = 0,
cond.discl.pers.beta.het = 0,
cond.diag.inst.beta.het = 0,
cond.discl.inst.beta.het = 0,
role.B.msm.prob = c(0.242, 0.321, 0.437),
role.BI.msm.prob = c(0.242, 0.321, 0.437),
role.H.msm.prob = c(0.242, 0.321, 0.437),
role.HI.msm.prob = c(0.242, 0.321, 0.437),
role.W.msm.prob = c(0.242, 0.321, 0.437),
role.B.msmf.prob = c(0.242, 0.321, 0.437),
role.BI.msmf.prob = c(0.242, 0.321, 0.437),
role.H.msmf.prob = c(0.242, 0.321, 0.437),
role.HI.msmf.prob = c(0.242, 0.321, 0.437),
role.W.msmf.prob = c(0.242, 0.321, 0.437),
vv.iev.B.prob = 0.47874,
vv.iev.BI.prob = 0.47874,
vv.iev.H.prob = 0.47874,
vv.iev.HI.prob = 0.47874,
vv.iev.W.prob = 0.47874,
##No PrEP in the SHAMP model.
prep.start = Inf,
prep.elig.model = "base",
prep.class.prob = c(0, 0, 0, 0),
prep.class.hr = c(1, 0.69, 0.19, 0.05),
prep.coverage = 0,
prep.cov.method = "curr",
prep.cov.rate = 1,
prep.tst.int = 90,
prep.risk.int = 182,
prep.risk.reassess = TRUE,
msm.temp.adjust = 1,
fa.temp.adjust = 1,
depart.adjust = 1,
return.adjust = 1,
immig.depart.BI.f = (1/(42*52*4))*depart.adjust,
immig.depart.HI.f = (1/(42*52*4))*depart.adjust,
immig.depart.BI.m = (1/(42*52*4))*depart.adjust,
immig.depart.HI.m = (1/(42*52*4))*depart.adjust,
immig.return.BI.f = (1/4) * return.adjust,
immig.return.HI.f = (1/4) * return.adjust,
immig.return.BI.m = (1/4) * return.adjust,
immig.return.HI.m = (1/4) * return.adjust,
immig.aq.prob.BI.f = .3*.06*((base.vi.main.BI.rate+base.vi.pers.BI.rate)/2)*URVI.prob * fa.temp.adjust,
immig.aq.prob.HI.f = .3*.005*((base.vi.main.HI.rate+base.vi.pers.HI.rate)/2)*URVI.prob * fa.temp.adjust ,
immig.aq.prob.BI.m = .3*.06*((base.vi.main.BI.rate+base.vi.pers.BI.rate)/2)*UIVI.prob * fa.temp.adjust ,
immig.aq.prob.HI.m = .3*.005*((base.vi.main.HI.rate+base.vi.pers.HI.rate)/2)*UIVI.prob * fa.temp.adjust ,
msm.aq.prob.B=0.000007183 * msm.temp.adjust,
msm.aq.prob.BI=0.000007183 * msm.temp.adjust,
msm.aq.prob.H=0.000007183 * msm.temp.adjust,
msm.aq.prob.HI=0.000007183 * msm.temp.adjust,
msm.aq.prob.W=0.000007183 * msm.temp.adjust,
demog.list=data.params[[1]]$demog.list,
demog.dist=data.params[[1]]$demog.dist,
sex.groups=data.params[[1]]$sex.groups,
race.groups=data.params[[1]]$race.groups,
age.groups=data.params[[1]]$age.groups,
age.adj=data.params[[1]]$age.adj,
...) {
p <- get_args(formal.args = formals(sys.function()),
dot.args = list(...))
if (!(testing.pattern %in% c("memoryless", "interval"))) {
stop("testing.pattern must be \"memoryless\" or \"interval\" ",
call. = FALSE)
}
p$time.unit <- time.unit
intvars <- grep(names(p), pattern = ".int", fixed = TRUE)
p[intvars] <- lapply(p[intvars], FUN = function(x) round(x / p$time.unit))
ratevars <- grep(names(p), pattern = ".rate", fixed = TRUE)
p[ratevars] <- lapply(p[ratevars], FUN = function(x) x * p$time.unit)
p$role.B.msm.prob <- role.B.msm.prob
p$role.BI.msm.prob <- role.BI.msm.prob
p$role.H.msm.prob <- role.H.msm.prob
p$role.HI.msm.prob <- role.HI.msm.prob
p$role.W.msm.prob <- role.W.msm.prob
p$role.B.msmf.prob <- role.B.msmf.prob
p$role.BI.msmf.prob <- role.BI.msmf.prob
p$role.H.msmf.prob <- role.H.msmf.prob
p$role.HI.msmf.prob <- role.HI.msmf.prob
p$role.W.msmf.prob <- role.W.msmf.prob
p$inst.trans.matrix <- matrix(1, nrow = 1)
p$role.trans.matrix <- matrix(c(1, 0, 0,
0, 1, 0,
0, 0, 1),
nrow = 3)
p$riskh.start <- max(1, prep.start - prep.risk.int - 1)
p$method <- method
p$modes <- 1
p$asmr.B.f <- asmr.B.f
p$asmr.BI.f <- asmr.BI.f
p$asmr.H.f <- asmr.H.f
p$asmr.HI.f <- asmr.HI.f
p$asmr.W.f <- asmr.W.f
p$asmr.B.m <- asmr.B.m
p$asmr.BI.m <- asmr.BI.m
p$asmr.H.m <- asmr.H.m
p$asmr.HI.m <- asmr.HI.m
p$asmr.W.m <- asmr.W.m
class(p) <- "param.net"
return(p)
}
#' @title Epidemic Model Initial Conditions for SHAMP
#'
#' @description Sets the initial conditions for a stochastic epidemic models
#' simulated with \code{\link{netsim}}.
#'
#' @param prev.R.S Initial disease prevalence among persons of group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S
#' where s is a charter (f, msf, msm, msmf)..
#' @param ... Additional arguments passed to function.
#'
#' @return
#' A list object of class \code{init_shamp}, which can be passed to EpiModel
#' function \code{\link{netsim}}.
#'
#' @keywords SHAMP
#'
#' @export
init_shamp <- function(prev.B.f = 0.02,
prev.BI.f =0.02,
prev.H.f =0.02,
prev.HI.f =0.02,
prev.W.f = 0.02,
prev.B.msf = 0.02,
prev.BI.msf =0.02,
prev.H.msf =0.02,
prev.HI.msf =0.02,
prev.W.msf = 0.02,
prev.B.msm = 0.02,
prev.BI.msm =0.02,
prev.H.msm =0.02,
prev.HI.msm =0.02,
prev.W.msm = 0.02,
prev.B.msmf = 0.02,
prev.BI.msmf =0.02,
prev.H.msmf =0.02,
prev.HI.msmf =0.02,
prev.W.msmf = 0.02,
...) {
p <- get_args(formal.args = formals(sys.function()),
dot.args = list(...))
p$init.prev.age.slope.B.f <- prev.B.f / 42
p$init.prev.age.slope.BI.f <- prev.BI.f / 42
p$init.prev.age.slope.H.f <- prev.H.f / 42
p$init.prev.age.slope.HI.f <- prev.HI.f / 42
p$init.prev.age.slope.W.f <- prev.W.f / 42
p$init.prev.age.slope.B.msf <- prev.B.msf / 42
p$init.prev.age.slope.BI.msf <- prev.BI.msf / 42
p$init.prev.age.slope.H.msf <- prev.H.msf / 42
p$init.prev.age.slope.HI.msf <- prev.HI.msf / 42
p$init.prev.age.slope.W.msf <- prev.W.msf / 42
p$init.prev.age.slope.B.msm <- prev.B.msm / 42
p$init.prev.age.slope.BI.msm <- prev.BI.msm / 42
p$init.prev.age.slope.H.msm <- prev.H.msm / 42
p$init.prev.age.slope.HI.msm <- prev.HI.msm / 42
p$init.prev.age.slope.W.msm <- prev.W.msm / 42
p$init.prev.age.slope.B.msmf <- prev.B.msmf / 42
p$init.prev.age.slope.BI.msmf <- prev.BI.msmf / 42
p$init.prev.age.slope.H.msmf <- prev.H.msmf / 42
p$init.prev.age.slope.HI.msmf <- prev.HI.msmf / 42
p$init.prev.age.slope.W.msmf <- prev.W.msmf / 42
class(p) <- "init.net"
return(p)
}
#' @title Epidemic Model Control Settings
#'
#' @description Sets the controls for stochastic network models simulated with
#' \code{\link{netsim}}.
#'
#' @param simno Unique ID for the simulation run, used for file naming purposes
#' if used in conjunction with the \code{EpiModelHPC} package.
#' @param nsims Number of simulations.
#' @param ncores Number of cores per run, if parallelization is used within the
#' \code{EpiModelHPC} package.
#' @param nsteps Number of time steps per simulation.
#' @param start Starting time step for simulation, with default to 1 to run new
#' simulation. This may also be set to 1 greater than the final time
#' step of a previous simulation to resume the simulation with different
#' parameters.
#' @param initialize.FUN Module function to use for initialization of the epidemic
#' model.
#' @param aging.FUN Module function for aging.
#' @param deaths.FUN Module function for general and disease-realted deaths.
#' @param births.FUN Module function for births or entries into the population.
#' @param test.FUN Module function for diagnostic disease testing.
#' @param tx.FUN Module function for ART initiation and adherence.
#' @param prep.FUN Module function for PrEP initiation and utilization.
#' @param progress.FUN Module function for HIV disease progression.
#' @param vl.FUN Module function for HIV viral load evolution.
#' @param aiclass.FUN Module function for one-off AI risk class transitions.
#' @param roleclass.FUN Module function for transitions in sexual roles.
#' @param resim_nets.FUN Module function for network resimulation at each time
#' step.
#' @param disclose.FUN Module function for HIV status disclosure.
#' @param acts.FUN Module function to simulate the number of sexual acts within
#' partnerships.
#' @param condoms.FUN Module function to simulate condom use within acts.
#' @param riskhist.FUN Module function to calculate risk history for uninfected
#' persons in the population.
#' @param position.FUN Module function to simulate sexual position within acts.
#' @param trans.FUN Module function to stochastically simulate disease transmission
#' over acts given individual and dyadic attributes.
#' @param heatbath.FUN Module function to stochastically simulate disease aquisition by msmf via
#' contact with an unsimulated msm population.
#' over acts given individual and dyadic attributes.
#' @param immigration.FUN Module function to stochastically simulate disease aquisition by migrants
#' via conatct with unsimulated populations in home counties during return trips.
#' @param prev.FUN Module function to calculate prevalence summary statistics.
#' @param verbose.FUN Module function to print model progress to the console or
#' external text files.
#' @param save.nwstats Calculate and save network statistics as defined in the
#' \code{simnet} modules.
#' @param verbose If \code{TRUE}, print out simulation progress to the console
#' if in interactive mode or text files if in batch mode.
#' @param verbose.int Integer specifying the interval between time steps at which
#' progress is printed.
#' @param ... Additional arguments passed to the function.
#'
#' @return
#' A list object of class \code{control_shamp}, which can be passed to the
#' EpiModel function \code{netsim}.
#'
#' @keywords SHAMP
#'
#' @export
control_shamp <- function(simno = 1,
nsims = 1,
ncores = 1,
nsteps = 100,
start = 1,
initialize.FUN = initialize_shamp,
aging.FUN = aging,
deaths.FUN = deaths_shamp,
births.FUN = births_shamp2,
demogupdate.FUN = demogupdate_shamp,
test.FUN = test_shamp,
tx.FUN = tx_shamp,
prep.FUN = prep_shamp,
progress.FUN = progress_msm,
vl.FUN = vl_shamp,
#aiclass.FUN = NULL,
#roleclass.FUN = NULL,
resim_nets.FUN = simnet_shamp,
disclose.FUN = disclose_shamp,
acts.FUN = acts_shamp,
condoms.FUN = condoms_shamp,
riskhist.FUN = riskhist_shamp,
position.FUN = position_shamp,
trans.FUN = trans_shamp,
heatbath.FUN = heatbath_msmf_shamp,
immigration.FUN = immigration_shamp,
prev.FUN = prevalence_shamp,
verbose.FUN = verbose_msm,
save.nwstats = FALSE,
verbose = TRUE,
verbose.int = 1,
...) {
formal.args <- formals(sys.function())
dot.args <- list(...)
p <- get_args(formal.args, dot.args)
p$skip.check <- TRUE
p$save.transmat <- FALSE
bi.mods <- grep(".FUN", names(formal.args), value = TRUE)
bi.mods <- bi.mods[which(sapply(bi.mods, function(x) !is.null(eval(parse(text = x))),
USE.NAMES = FALSE) == TRUE)]
p$bi.mods <- bi.mods
p$user.mods <- grep(".FUN", names(dot.args), value = TRUE)
p$save.other = c("attr", "temp", "el", "p")
p$save.network = FALSE
class(p) <- "control.net"
return(p)
}
dth2/EpiModelHIV_SHAMP documentation built on May 15, 2019, 4:56 p.m.
R Package Documentation
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# SHAMP -----------------------------------------------------------------
#' @title Epidemic Model Parameters for SHAMP
#'
#' @description Sets the epidemic parameters for stochastic network models
#' simulated with \code{\link{netsim}} for EpiModelHIV
#'
#' @param last.neg.test.R.S.int Time range in days for last negative test for
#' race group R where R is a charater (B, BI, H, HI or W) and sex group S
#' where s is a charter (f, msf, msm, msmf).
#' @param mean.test.B.int Mean intertest interval in days for race group R where R
#' is a charater (B, BI, H, HI or W) and sex group S
#' where s is a charter (f, msf, msm, msmf) who test.
#' @param testing.pattern Method for HIV testing, with options \code{"memoryless"}
#' for constant hazard without regard to time since previous test, or
#' \code{"interval"} deterministic fixed intervals.
#' @param test.window.int Length of the HIV test window period in days.
#' @param tt.traj.R.S.prob Proportion of race group R, where R is a charater (B, BI, H, HI or W),
#' and sex group S, where s is a charter (f, msf, msm, msmf), who enter one of four
#' testing/treatment trajectories: never test or treat, test and never
#' initiate treatment, test and treated with partial viral suppression,
#' and test and treated with full suppression.
#' @param tx.init.R.S.prob Probability per time step that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' who has tested positive will initiate treatment.
#' @param tx.halt.R.S.prob Probability per time step that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' who is currently on treatment will halt treatment.
#' @param tx.reinit.R.S.prob Probability per time step that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' who is not currently on treatment but who has been in the past will
#' re-initiate treatment.
#' @param max.time.off.tx.full.int Number of days off treatment for a full
#' suppressor before onset of AIDS, including time before diagnosis.
#' @param max.time.on.tx.part.int Number of days on treatment for a
#' partial suppressor beofre onset of AIDS.
#' @param max.time.off.tx.part.int Nnumber of days off treatment for a
#' partial suppressor before onset of AIDS, including time before
#' diagnosis.
#' @param vl.acute.rise.int Number of days to peak viremia during acute
#' infection.
#' @param vl.acute.peak Peak viral load (in log10 units) at the height of acute
#' infection.
#' @param vl.acute.fall.int Number of days from peak viremia to set-point
#' viral load during the acute infection period.
#' @param vl.set.point Set point viral load (in log10 units).
#' @param vl.aids.onset.int Number of days to AIDS for a treatment-naive
#' patient.
#' @param vl.aids.int Duration of AIDS stage infection in days.
#' @param vl.fatal Viral load in AIDS at which death occurs.
#' @param vl.full.supp Log10 viral load at full suppression on ART.
#' @param vl.part.supp Log10 viral load at partial suppression on ART.
#' @param full.supp.down.slope For full suppressors, number of log10 units that
#' viral load falls per time step from treatment initiation or re-initiation
#' until the level in \code{vl.full.supp}.
#' @param full.supp.up.slope For full suppressors, number of log10 units that
#' viral load rises per time step from treatment halting until expected
#' value.
#' @param part.supp.down.slope For partial suppressors, number of log10 units
#' that viral load falls per time step from treatment initiation or
#' re-initiation until the level in \code{vl.part.supp}.
#' @param part.supp.up.slope For partial suppressors, number of log10 units that
#' viral load rises per time step from treatment halting until expected value.
#' @param b.R.S.rate Rate at which a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' enters the population.
#' @param birth.age Age (in years) of new arrivals.
#' @param b.method Method for calculating the number of expected births at each
#' time step, with \code{"fixed"} based on the number of persons at the
#' initial time step and \code{"varying"} based on the current time step.
#' @param age.adj This value is added to the squareroot of age for females in sqrt.age.adj
#' so that sex age asymmetry can be captured with an absdiff term
#' @param msm.frac The fraction of males entering the population at each time step
#' as a male that has intercourse exclusivly with other males "msm" -
#' males not designated as msm or msmf are considered "msf".
#' @param msmf.frac The fraction of males entering the population at each time step as
#' as a male that has intercourse with both males and females "msmf" -
#' males not designated as msm or msmf are considered "msf".
#' @param URAI.prob Probability of transmission for a man having unprotected
#' receptive anal intercourse with an infected man at set point viral
#' load.
#' @param UIAI.prob Probability of transmission for an uncircumcised man having
#' unprotected insertive anal intercourse with an infected man at set
#' point viral load.
#' @param URVI.prob Probability of transmission for a woman having unprotected
#' receptive vaginal intercourse with an infected man at set point viral
#' load.
#' @param UIVI.prob Probability of transmission for an uncircumcised man having
#' unprotected insertive vaginal intercourse with an infected women at set
#' point viral load.
#' @param acute.rr Relative risk of infection (compared to that predicted by
#' elevated viral load) when positive partner is in the acute stage.
#' @param circ.rr Relative risk of infection from insertive anal sex when the
#' negative insertive partner is circumcised.
#' @param condom.rr Relative risk of infection from anal sex when a condom is
#' used.
#' @param disc.outset.main.R.S.prob Probability that an HIV-infected person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), will disclose their status at the start of a main partnership.
#' @param disc.at.diag.main.R.S.prob Probability that a a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' already in a main partnership will disclose at the time of diagnosis.
#' @param disc.post.diag.main.R.S.prob Probability that an HIV-infected a person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), in a main partnership will disclose their status, assuming they didn't
#' at the start of the partnership or at diagnosis.
#' @param disc.outset.pers.R.S.prob Probability that an HIV-infected a person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), will disclose their status at the start of a casual partnership.
#' @param disc.at.diag.pers.R.S.prob Probability that a person in race group R, where R is a
#' charater (B, BI, H, HI or W), and sex group S, where s is a charter (f, msf, msm, msmf),
#' already in a casual partnership will disclose at the time of diagnosis.
#' @param disc.post.diag.pers.R.S.prob Probability that an HIV-infected person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), in a casual partnership will disclose their status, assuming they
#' didn't at the start of the partnership or at diagnosis.
#' @param disc.inst.R.S.prob Probability that an HIV-infected person in race group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S, where s is a charter
#' (f, msf, msm, msmf), will disclose ther status to a one-off partner.
#' @param circ.R.prob Probablity that a male in race group R, where R is a
#' charater (B, BI, H, HI or W), newly arriving in the population
#' will be circumcised.
#' @param ccr5.R.S.prob Vector of length two of frequencies of the Delta 32
#' mutation (homozygous and heterozygous, respectively) in the CCR5 gene
#' among people in race group R, where R is a charater (B, BI, H, HI or W),
#' and sex group S, where s is a charter (f or m).
#' @param ccr5.heteroz.rr Relative risk of infection for people who are heterozygous
#' in the CCR5 mutation.
#' @param num.inst.ai.classes Number of quantiles into which men should be
#' divided in determining their levels of one-off anal intercourse.
#' @param base.ai.main.R.rate Expected coital frequency (for anal intercourse) for males
#' (msm and msmf) in race group R, where R is a charater (B, BI, H, HI or W),
#' in main partnerships (acts per day).
#' @param base.vi.main.R.rate Expected coital frequency (for vaginal intercourse) for
#' males and females in race group R, where R is a charater (B, BI, H, HI or W),
#' in main partnerships (acts per day).
#' @param base.ai.pers.R.rate Expected coital frequency (for anal intercourse) for males
#' (msm and msmf) in race group R, where R is a charater (B, BI, H, HI or W),
#' in casual partnerships (acts per day).
#' @param base.vi.pers.R.rate Expected coital frequency (for vaginal intercourse) for
#' males and females in race group R, where R is a charater (B, BI, H, HI or W),
#' in casual partnerships (acts per day).
#' @param ai.scale General relative scaler for all ai act rates for model
#' calibration.
#' @param vi.scale General relative scaler for all vi act rates for model
#' calibration.
#' @param cond.main.R.prob.msm Probability of condom use by an male of race group R,
#' where R is a charater (B, BI, H, HI or W), in a male-male main partnership.
#' @param cond.pers.always.prob.msm Fraction of men in male-male casual partnerships who always
#' use condoms in those partnerships.
#' @param cond.pers.R.prob.msm Of men who are not consistent condom users, per-act
#' probability of condom use in a casual male-male partnership by
#' males of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.inst.always.prob.msm Fraction of men in male-male one-time partnerships who always
#' use condoms in those partnerships.
#' @param cond.inst.R.prob.msm Of men who are not consistent condom users, per-act
#' probability of condom use in a one-time male-male partnership by
#' males of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.always.prob.corr.msm Correlation coefficient for probability of always
#' using condoms in both casual and one-off male-male partnerships
#' @param cond.main.R.prob.het Probability of condom use by an person of race group R,
#' where R is a charater (B, BI, H, HI or W), in a heterosexual main partnership.
#' @param cond.pers.always.prob.het Fraction of people in heterosexual casual partnerships who always
#' use condoms in those partnerships.
#' @param cond.pers.R.prob.het Of people who are not consistent condom users, per-act
#' probability of condom use in a casual heterosexual partnership by
#' people of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.inst.always.prob.het Fraction of people in heterosexual one-time partnerships who always
#' use condoms in those partnerships.
#' @param cond.inst.R.prob.het Of people who are not consistent condom users, per-act
#' probability of condom use in a one-time heterosexual partnership by
#' males of race group R, where R is a charater (B, BI, H, HI or W).
#' @param cond.always.prob.corr.het Correlation coefficient for probability of always
#' using condoms in both casual and one-off heterosexual partnerships
#' @param cond.diag.main.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male main partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.main.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male main partnership if the HIV-infected man has disclosed.
#' @param cond.diag.pers.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male casual partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.pers.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male casual partnership if the HIV-infected man has disclosed
#' his status.
#' @param cond.diag.inst.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male one-off partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.inst.beta.msm Beta multiplier for the log odds of using a
#' condom in a male-male one-off partnership if the HIV-infected man has disclosed
#' his status.
#' @param cond.diag.main.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual main partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.main.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual main partnership if the HIV-infected man has disclosed.
#' @param cond.diag.pers.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual casual partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.pers.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual casual partnership if the HIV-infected man has disclosed
#' his status.
#' @param cond.diag.inst.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual one-off partnership if the HIV-infected man has been
#' diagnosed.
#' @param cond.discl.inst.beta.het Beta multiplier for the log odds of using a
#' condom in a heterosexual one-off partnership if the HIV-infected man has disclosed
#' his status.
#' @param vv.iev.R.prob Probability that a person of group R, where R is a charater (B, BI, H, HI or W),
#' in a male male relationship will engage in intra-event versatility
#' ("flipping") given that they're having AI.
#' @param prep.start Time step at which the PrEP intervention should start.
#' @param prep.elig.model Modeling approach for determining who is eligible for
#' PrEP. Current options are limited to: \code{"all"} for all persons who
#' have never been on PrEP and are disease-susceptible.
#' @param prep.class.prob The probability of adherence class in non-adherent,
#' low adherence, medium adherence, or high adherence groups (from Liu).
#' @param prep.class.hr The hazard ratio for infection per act associated with each
#' level of adherence (from Grant).
#' @param prep.coverage The proportion of the eligible population who are start
#' PrEP once they become eligible.
#' @param prep.cov.method The method for calculating PrEP coverage, with options
#' of \code{"curr"} to base the numerator on the number of people currently
#' on PrEP and \code{"ever"} to base it on the number of people ever on
#' PrEP.
#' @param prep.cov.rate The rate at which persons initiate PrEP conditional on
#' their eligibility, with 1 equal to instant start.
#' @param prep.tst.int Testing interval for those who are actively on PrEP. This
#' overrides the mean testing interval parameters.
#' @param prep.risk.int Time window for assessment of risk eligibility for PrEP
#' in days.
#' @param prep.risk.reassess If \code{TRUE}, reassess eligibility for PrEP at
#' each testing visit.
#'
#' @param immig.depart.R.S The probability per time step that a person of group R,
#' where R is a charater (BI or HI), and sex S, were S is a charater (f or m), leaves the population
#' being simulated by returning to their home country.
#' @param immig.return.R.S The probability per time step that a person of group R,
#' where R is a charater (BI or HI), and sex S, were S is a charater (f or m), who has left
#' the population being simulated by returning to their home country returns to the population
#' being simulated.
#' @param immig.aq.prob.R.S The probability per time step that a person of group R,
#' where R is a charater (BI or HI), and sex S, were S is a charater (f or m), who has left
#' the population being simulated aquires HIV while in their home country.
#'
#'
#'
#' @param ... Additional arguments passed to the function.
#'
#' @return
#' A list object of class \code{param_shamp}, which can be passed to
#' EpiModel function \code{netsim}.
#'
#' @keywords shamp
#'
#' @export
param_shamp <- function(race.method = 1,
time.unit = 7,
method=1,
last.neg.test.B.f.int = 1464,
last.neg.test.BI.f.int = 1464,
last.neg.test.H.f.int = 1464,
last.neg.test.HI.f.int = 1464,
last.neg.test.W.f.int = 1464,
last.neg.test.B.msf.int = 1708,
last.neg.test.BI.msf.int = 1708,
last.neg.test.H.msf.int = 1708,
last.neg.test.HI.msf.int = 1708,
last.neg.test.W.msf.int = 1708,
last.neg.test.B.msm.int = 1037,
last.neg.test.BI.msm.int = 1037,
last.neg.test.H.msm.int = 1037,
last.neg.test.HI.msm.int = 1037,
last.neg.test.W.msm.int = 1037,
last.neg.test.B.msmf.int = 1129,
last.neg.test.BI.msmf.int = 1129,
last.neg.test.H.msmf.int = 1129,
last.neg.test.HI.msmf.int = 1129,
last.neg.test.W.msmf.int = 1129,
mean.test.B.f.int = 1464,
mean.test.BI.f.int = 1464,
mean.test.H.f.int = 1464,
mean.test.HI.f.int = 1464,
mean.test.W.f.int = 1464,
mean.test.B.msf.int = 1708,
mean.test.BI.msf.int = 1708,
mean.test.H.msf.int = 1708,
mean.test.HI.msf.int = 1708,
mean.test.W.msf.int = 1708,
mean.test.B.msm.int = 1037,
mean.test.BI.msm.int = 1037,
mean.test.H.msm.int = 1037,
mean.test.HI.msm.int = 1037,
mean.test.W.msm.int = 1037,
mean.test.B.msmf.int = 1129,
mean.test.BI.msmf.int = 1129,
mean.test.H.msmf.int = 1129,
mean.test.HI.msmf.int = 1129,
mean.test.W.msmf.int = 1129,
testing.pattern = "memoryless",
test.window.int = 15,
tt.traj.B.f.prob = c(0.741, 0.000, 0.053, 0.206),
tt.traj.BI.f.prob = c(0.741, 0.000, 0.053, 0.206),
tt.traj.H.f.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.HI.f.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.W.f.prob = c(0.741, 0.000, 0.057, 0.202),
tt.traj.B.msf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.BI.msf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.H.msf.prob = c(0.741, 0.000, 0.067, 0.192),
tt.traj.HI.msf.prob = c(0.741, 0.000, 0.067, 0.192),
tt.traj.W.msf.prob = c(0.741, 0.000, 0.052, 0.207),
tt.traj.B.msm.prob = c(.025, 0, .065, .911),
tt.traj.BI.msm.prob = c(.025, 0, .065, .911),
tt.traj.H.msm.prob = c(.025, 0, .065, .911),
tt.traj.HI.msm.prob = c(.025, 0, .065, .911),
tt.traj.W.msm.prob = c(.025, 0, .065, .911),
tt.traj.B.msmf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.BI.msmf.prob = c(0.741, 0.000, 0.066, 0.193),
tt.traj.H.msmf.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.HI.msmf.prob = c(0.741, 0.000, 0.041, 0.218),
tt.traj.W.msmf.prob = c(0.741, 0.000, 0.052, 0.207),
tx.init.B.f.prob = 0.2331,
tx.init.BI.f.prob = 0.2331,
tx.init.H.f.prob = 0.2331,
tx.init.HI.f.prob = 0.2331,
tx.init.W.f.prob = 0.2331,
tx.init.B.msf.prob = 0.2331,
tx.init.BI.msf.prob = 0.2331,
tx.init.H.msf.prob = 0.2331,
tx.init.HI.msf.prob = 0.2331,
tx.init.W.msf.prob = 0.2331,
tx.init.B.msm.prob = 0.1467,
tx.init.BI.msm.prob = 0.1467,
tx.init.H.msm.prob = 0.1467,
tx.init.HI.msm.prob = 0.1467,
tx.init.W.msm.prob = 0.1467,
tx.init.B.msmf.prob = 0.2331,
tx.init.BI.msmf.prob = 0.2331,
tx.init.H.msmf.prob = 0.2331,
tx.init.HI.msmf.prob = 0.2331,
tx.init.W.msmf.prob = 0.2331,
#No halting in the basic SHAMP model
tx.halt.B.f.prob = 0.0001,
tx.halt.BI.f.prob = 0.0001,
tx.halt.H.f.prob = 0.0001,
tx.halt.HI.f.prob = 0.0001,
tx.halt.W.f.prob = 0.0001,
tx.halt.B.msf.prob = 0.0001,
tx.halt.BI.msf.prob = 0.0001,
tx.halt.H.msf.prob = 0.0001,
tx.halt.HI.msf.prob = 0.0001,
tx.halt.W.msf.prob = 0.0001,
tx.halt.B.msm.prob = 0.0001,
tx.halt.BI.msm.prob = 0.0001,
tx.halt.H.msm.prob = 0.0001,
tx.halt.HI.msm.prob = 0.0001,
tx.halt.W.msm.prob = 0.0001,
tx.halt.B.msmf.prob = 0.0001,
tx.halt.BI.msmf.prob = 0.0001,
tx.halt.H.msmf.prob = 0.0001,
tx.halt.HI.msmf.prob = 0.0001,
tx.halt.W.msmf.prob = 0.0001,
##With no halting there is also no re-initiation
tx.reinit.B.f.prob = 1,
tx.reinit.BI.f.prob = 1,
tx.reinit.H.f.prob = 1,
tx.reinit.HI.f.prob = 1,
tx.reinit.W.f.prob = 1,
tx.reinit.B.msf.prob = 1,
tx.reinit.BI.msf.prob = 1,
tx.reinit.H.msf.prob = 1,
tx.reinit.HI.msf.prob = 1,
tx.reinit.W.msf.prob = 1,
tx.reinit.B.msm.prob = 1,
tx.reinit.BI.msm.prob = 1,
tx.reinit.H.msm.prob = 1,
tx.reinit.HI.msm.prob = 1,
tx.reinit.W.msm.prob = 1,
tx.reinit.B.msmf.prob = 1,
tx.reinit.BI.msmf.prob = 1,
tx.reinit.H.msmf.prob = 1,
tx.reinit.HI.msmf.prob = 1,
tx.reinit.W.msmf.prob = 1,
max.time.off.tx.full.int = 520 * 7,
max.time.on.tx.part.int = 52 * 15 * 7,
max.time.off.tx.part.int = 520 * 7,
vl.acute.rise.int = 45,
vl.acute.peak = 6.886,
vl.acute.fall.int = 21,
vl.set.point = 4.5,
vl.aids.onset.int = 520 * 7,
vl.aids.int = 52 * 2 * 7,
vl.fatal = 7,
vl.full.supp = 1.5,
vl.part.supp = 3.5,
full.supp.down.slope = 0.25,
full.supp.up.slope = 0.25,
part.supp.down.slope = 0.25,
part.supp.up.slope = 0.25,
#Births are set for consistent population demographics.
#b.B.f.rate = 1e-3 / 7,
#b.BI.f.rate = 1e-3 / 7,
#b.H.f.rate = 1e-3 / 7,
#b.HI.f.rate = 1e-3 / 7,
#b.W.f.rate = 1e-3 / 7,
#b.B.m.rate = 1e-3 / 7,
#b.BI.m.rate = 1e-3 / 7,
#b.H.m.rate = 1e-3 / 7,
#b.HI.m.rate = 1e-3 / 7,
#b.W.m.rate = 1e-3 / 7,
birth.age = 18,
exit.age = 60,
b.method = "fixed",
msm.frac=0.0,
msmf.frac=0.012,
URAI.prob = 138/10000,
UIAI.prob = 11/10000,
URVI.prob = 8/10000,
UIVI.prob = 4/10000,
acute.rr = 4,
circ.rr = .4,
condom.rr = 0.25,
##For now aasume no disclosure (may use for calibration)
disc.outset.main.B.f.prob = 0,
disc.outset.main.BI.f.prob = 0,
disc.outset.main.H.f.prob = 0,
disc.outset.main.HI.f.prob = 0,
disc.outset.main.W.f.prob = 0,
disc.outset.main.B.msf.prob = 0,
disc.outset.main.BI.msf.prob = 0,
disc.outset.main.H.msf.prob = 0,
disc.outset.main.HI.msf.prob = 0,
disc.outset.main.W.msf.prob = 0,
disc.outset.main.B.msm.prob = 0,
disc.outset.main.BI.msm.prob = 0,
disc.outset.main.H.msm.prob = 0,
disc.outset.main.HI.msm.prob = 0,
disc.outset.main.W.msm.prob = 0,
disc.outset.main.B.msmf.prob = 0,
disc.outset.main.BI.msmf.prob = 0,
disc.outset.main.H.msmf.prob = 0,
disc.outset.main.HI.msmf.prob = 0,
disc.outset.main.W.msmf.prob = 0,
disc.at.diag.main.B.f.prob = 0,
disc.at.diag.main.BI.f.prob = 0,
disc.at.diag.main.H.f.prob = 0,
disc.at.diag.main.HI.f.prob = 0,
disc.at.diag.main.W.f.prob = 0,
disc.at.diag.main.B.msf.prob = 0,
disc.at.diag.main.BI.msf.prob = 0,
disc.at.diag.main.H.msf.prob = 0,
disc.at.diag.main.HI.msf.prob = 0,
disc.at.diag.main.W.msf.prob = 0,
disc.at.diag.main.B.msm.prob = 0,
disc.at.diag.main.BI.msm.prob = 0,
disc.at.diag.main.H.msm.prob = 0,
disc.at.diag.main.HI.msm.prob = 0,
disc.at.diag.main.W.msm.prob = 0,
disc.at.diag.main.B.msmf.prob = 0,
disc.at.diag.main.BI.msmf.prob = 0,
disc.at.diag.main.H.msmf.prob = 0,
disc.at.diag.main.HI.msmf.prob = 0,
disc.at.diag.main.W.msmf.prob = 0,
disc.post.diag.main.B.f.prob = 0,
disc.post.diag.main.BI.f.prob = 0,
disc.post.diag.main.H.f.prob = 0,
disc.post.diag.main.HI.f.prob = 0,
disc.post.diag.main.W.f.prob = 0,
disc.post.diag.main.B.msf.prob = 0,
disc.post.diag.main.BI.msf.prob = 0,
disc.post.diag.main.H.msf.prob = 0,
disc.post.diag.main.HI.msf.prob = 0,
disc.post.diag.main.W.msf.prob = 0,
disc.post.diag.main.B.msm.prob = 0,
disc.post.diag.main.BI.msm.prob = 0,
disc.post.diag.main.H.msm.prob = 0,
disc.post.diag.main.HI.msm.prob = 0,
disc.post.diag.main.W.msm.prob = 0,
disc.post.diag.main.B.msmf.prob = 0,
disc.post.diag.main.BI.msmf.prob = 0,
disc.post.diag.main.H.msmf.prob = 0,
disc.post.diag.main.HI.msmf.prob = 0,
disc.post.diag.main.W.msmf.prob = 0,
disc.outset.pers.B.f.prob = 0,
disc.outset.pers.BI.f.prob = 0,
disc.outset.pers.H.f.prob = 0,
disc.outset.pers.HI.f.prob = 0,
disc.outset.pers.W.f.prob = 0,
disc.outset.pers.B.msf.prob = 0,
disc.outset.pers.BI.msf.prob = 0,
disc.outset.pers.H.msf.prob = 0,
disc.outset.pers.HI.msf.prob = 0,
disc.outset.pers.W.msf.prob = 0,
disc.outset.pers.B.msm.prob = 0,
disc.outset.pers.BI.msm.prob = 0,
disc.outset.pers.H.msm.prob = 0,
disc.outset.pers.HI.msm.prob = 0,
disc.outset.pers.W.msm.prob = 0,
disc.outset.pers.B.msmf.prob = 0,
disc.outset.pers.BI.msmf.prob = 0,
disc.outset.pers.H.msmf.prob = 0,
disc.outset.pers.HI.msmf.prob = 0,
disc.outset.pers.W.msmf.prob = 0,
disc.at.diag.pers.B.f.prob = 0,
disc.at.diag.pers.BI.f.prob = 0,
disc.at.diag.pers.H.f.prob = 0,
disc.at.diag.pers.HI.f.prob = 0,
disc.at.diag.pers.W.f.prob = 0,
disc.at.diag.pers.B.msf.prob = 0,
disc.at.diag.pers.BI.msf.prob = 0,
disc.at.diag.pers.H.msf.prob = 0,
disc.at.diag.pers.HI.msf.prob = 0,
disc.at.diag.pers.W.msf.prob = 0,
disc.at.diag.pers.B.msm.prob = 0,
disc.at.diag.pers.BI.msm.prob = 0,
disc.at.diag.pers.H.msm.prob = 0,
disc.at.diag.pers.HI.msm.prob = 0,
disc.at.diag.pers.W.msm.prob = 0,
disc.at.diag.pers.B.msmf.prob = 0,
disc.at.diag.pers.BI.msmf.prob = 0,
disc.at.diag.pers.H.msmf.prob = 0,
disc.at.diag.pers.HI.msmf.prob = 0,
disc.at.diag.pers.W.msmf.prob = 0,
disc.post.diag.pers.B.f.prob = 0,
disc.post.diag.pers.BI.f.prob = 0,
disc.post.diag.pers.H.f.prob = 0,
disc.post.diag.pers.HI.f.prob = 0,
disc.post.diag.pers.W.f.prob = 0,
disc.post.diag.pers.B.msf.prob = 0,
disc.post.diag.pers.BI.msf.prob = 0,
disc.post.diag.pers.H.msf.prob = 0,
disc.post.diag.pers.HI.msf.prob = 0,
disc.post.diag.pers.W.msf.prob = 0,
disc.post.diag.pers.B.msm.prob = 0,
disc.post.diag.pers.BI.msm.prob = 0,
disc.post.diag.pers.H.msm.prob = 0,
disc.post.diag.pers.HI.msm.prob = 0,
disc.post.diag.pers.W.msm.prob = 0,
disc.post.diag.pers.B.msmf.prob = 0,
disc.post.diag.pers.BI.msmf.prob = 0,
disc.post.diag.pers.H.msmf.prob = 0,
disc.post.diag.pers.HI.msmf.prob = 0,
disc.post.diag.pers.W.msmf.prob = 0,
disc.inst.B.f.prob = 0,
disc.inst.BI.f.prob = 0,
disc.inst.H.f.prob = 0,
disc.inst.HI.f.prob = 0,
disc.inst.W.f.prob = 0,
disc.inst.B.msf.prob = 0,
disc.inst.BI.msf.prob = 0,
disc.inst.H.msf.prob = 0,
disc.inst.HI.msf.prob = 0,
disc.inst.W.msf.prob = 0,
disc.inst.B.msm.prob = 0,
disc.inst.BI.msm.prob = 0,
disc.inst.H.msm.prob = 0,
disc.inst.HI.msm.prob = 0,
disc.inst.W.msm.prob = 0,
disc.inst.B.msmf.prob = 0,
disc.inst.BI.msmf.prob = 0,
disc.inst.H.msmf.prob = 0,
disc.inst.HI.msmf.prob = 0,
disc.inst.W.msmf.prob = 0,
circ.B.prob = 0.770,
circ.BI.prob = 0.450,
circ.H.prob = 0.480,
circ.HI.prob = 0.360,
circ.W.prob = 0.910,
ccr5.B.f.prob = c(0, 0.0),
ccr5.BI.f.prob = c(0, 0.0),
ccr5.H.f.prob = c(0, 0.0),
ccr5.HI.f.prob = c(0, 0.0),
ccr5.W.f.prob = c(0.017, 0.0),
ccr5.B.m.prob = c(0, 0.0),
ccr5.BI.m.prob = c(0, 0.0),
ccr5.H.m.prob = c(0, 0.0),
ccr5.HI.m.prob = c(0, 0.0),
ccr5.W.m.prob = c(0.017, 0.0),
ccr5.heteroz.rr = 0,
num.inst.ai.classes = 1,
#not modeling msm but will include home testing SEattle estimates as placeholders
base.ai.main.B.rate = 1.379/7,
base.ai.main.BI.rate = 1.379/7,
base.ai.main.H.rate = 1.379/7,
base.ai.main.HI.rate = 1.379/7,
base.ai.main.W.rate = 1.379/7,
base.ai.pers.B.rate = 0.883/7,
base.ai.pers.BI.rate = 0.883/7,
base.ai.pers.H.rate = 0.883/7,
base.ai.pers.HI.rate = 0.883/7,
base.ai.pers.W.rate = 0.883/7,
base.vi.main.B.rate = 0.25 ,
base.vi.main.BI.rate = 0.19 ,
base.vi.main.H.rate = 0.26,
base.vi.main.HI.rate = 0.24,
base.vi.main.W.rate = 0.22,
base.vi.pers.B.rate = 0.12,
base.vi.pers.BI.rate = 0.09,
base.vi.pers.H.rate = 0.12,
base.vi.pers.HI.rate = 0.09,
base.vi.pers.W.rate = 0.12,
ai.scale = 1,
vi.scale = 1,
# For MSM pulled data from the mobile study but we are not modeling MSM for now.
cond.main.B.prob.msm = 0.062,
cond.main.BI.prob.msm = 0.062,
cond.main.H.prob.msm = 0.062,
cond.main.HI.prob.msm = 0.062,
cond.main.W.prob.msm = 0.062,
cond.pers.always.prob.msm = 0.01,
cond.pers.B.prob.msm = 0.0304,
cond.pers.BI.prob.msm = 0.0304,
cond.pers.H.prob.msm = 0.0304,
cond.pers.HI.prob.msm = 0.0304,
cond.pers.W.prob.msm = 0.0304,
cond.inst.always.prob.msm = 0.01,
cond.inst.B.prob.msm = 0.413,
cond.inst.BI.prob.msm = 0.413 ,
cond.inst.H.prob.msm = 0.413 ,
cond.inst.HI.prob.msm = 0.413 ,
cond.inst.W.prob.msm = 0.413 ,
cond.always.prob.corr.msm = 0.01,
cond.main.B.prob.het = 0.12,
cond.main.BI.prob.het = 0.17,
cond.main.H.prob.het = 0.17,
cond.main.HI.prob.het = 0.19,
cond.main.W.prob.het = 0.16,
cond.pers.always.prob.het = .0001,
cond.pers.B.prob.het = 0.54,
cond.pers.BI.prob.het = 0.65,
cond.pers.H.prob.het = 0.5,
cond.pers.HI.prob.het = 0.49,
cond.pers.W.prob.het = 0.48,
cond.inst.always.prob.het = .0001,
cond.inst.B.prob.het = 0.85,
cond.inst.BI.prob.het = 0.73,
cond.inst.H.prob.het = 0.78,
cond.inst.HI.prob.het = 0.76,
cond.inst.W.prob.het = 0.72,
cond.always.prob.corr.het = .0001,
#WAITING ON APRIL AS POSIBLE DATA SOURCE.
#Currect placeholder from home testing project
cond.diag.main.beta.msm = -0.67,
cond.discl.main.beta.msm = -0.85,
cond.diag.pers.beta.msm = -0.67,
cond.discl.pers.beta.msm = -0.85,
cond.diag.inst.beta.msm = -0.67,
cond.discl.inst.beta.msm = -0.85,
cond.diag.main.beta.het = 0,
cond.discl.main.beta.het = 0,
cond.diag.pers.beta.het = 0,
cond.discl.pers.beta.het = 0,
cond.diag.inst.beta.het = 0,
cond.discl.inst.beta.het = 0,
role.B.msm.prob = c(0.242, 0.321, 0.437),
role.BI.msm.prob = c(0.242, 0.321, 0.437),
role.H.msm.prob = c(0.242, 0.321, 0.437),
role.HI.msm.prob = c(0.242, 0.321, 0.437),
role.W.msm.prob = c(0.242, 0.321, 0.437),
role.B.msmf.prob = c(0.242, 0.321, 0.437),
role.BI.msmf.prob = c(0.242, 0.321, 0.437),
role.H.msmf.prob = c(0.242, 0.321, 0.437),
role.HI.msmf.prob = c(0.242, 0.321, 0.437),
role.W.msmf.prob = c(0.242, 0.321, 0.437),
vv.iev.B.prob = 0.47874,
vv.iev.BI.prob = 0.47874,
vv.iev.H.prob = 0.47874,
vv.iev.HI.prob = 0.47874,
vv.iev.W.prob = 0.47874,
##No PrEP in the SHAMP model.
prep.start = Inf,
prep.elig.model = "base",
prep.class.prob = c(0, 0, 0, 0),
prep.class.hr = c(1, 0.69, 0.19, 0.05),
prep.coverage = 0,
prep.cov.method = "curr",
prep.cov.rate = 1,
prep.tst.int = 90,
prep.risk.int = 182,
prep.risk.reassess = TRUE,
msm.temp.adjust = 1,
fa.temp.adjust = 1,
depart.adjust = 1,
return.adjust = 1,
immig.depart.BI.f = (1/(42*52*4))*depart.adjust,
immig.depart.HI.f = (1/(42*52*4))*depart.adjust,
immig.depart.BI.m = (1/(42*52*4))*depart.adjust,
immig.depart.HI.m = (1/(42*52*4))*depart.adjust,
immig.return.BI.f = (1/4) * return.adjust,
immig.return.HI.f = (1/4) * return.adjust,
immig.return.BI.m = (1/4) * return.adjust,
immig.return.HI.m = (1/4) * return.adjust,
immig.aq.prob.BI.f = .3*.06*((base.vi.main.BI.rate+base.vi.pers.BI.rate)/2)*URVI.prob * fa.temp.adjust,
immig.aq.prob.HI.f = .3*.005*((base.vi.main.HI.rate+base.vi.pers.HI.rate)/2)*URVI.prob * fa.temp.adjust ,
immig.aq.prob.BI.m = .3*.06*((base.vi.main.BI.rate+base.vi.pers.BI.rate)/2)*UIVI.prob * fa.temp.adjust ,
immig.aq.prob.HI.m = .3*.005*((base.vi.main.HI.rate+base.vi.pers.HI.rate)/2)*UIVI.prob * fa.temp.adjust ,
msm.aq.prob.B=0.000007183 * msm.temp.adjust,
msm.aq.prob.BI=0.000007183 * msm.temp.adjust,
msm.aq.prob.H=0.000007183 * msm.temp.adjust,
msm.aq.prob.HI=0.000007183 * msm.temp.adjust,
msm.aq.prob.W=0.000007183 * msm.temp.adjust,
demog.list=data.params[[1]]$demog.list,
demog.dist=data.params[[1]]$demog.dist,
sex.groups=data.params[[1]]$sex.groups,
race.groups=data.params[[1]]$race.groups,
age.groups=data.params[[1]]$age.groups,
age.adj=data.params[[1]]$age.adj,
...) {
p <- get_args(formal.args = formals(sys.function()),
dot.args = list(...))
if (!(testing.pattern %in% c("memoryless", "interval"))) {
stop("testing.pattern must be \"memoryless\" or \"interval\" ",
call. = FALSE)
}
p$time.unit <- time.unit
intvars <- grep(names(p), pattern = ".int", fixed = TRUE)
p[intvars] <- lapply(p[intvars], FUN = function(x) round(x / p$time.unit))
ratevars <- grep(names(p), pattern = ".rate", fixed = TRUE)
p[ratevars] <- lapply(p[ratevars], FUN = function(x) x * p$time.unit)
p$role.B.msm.prob <- role.B.msm.prob
p$role.BI.msm.prob <- role.BI.msm.prob
p$role.H.msm.prob <- role.H.msm.prob
p$role.HI.msm.prob <- role.HI.msm.prob
p$role.W.msm.prob <- role.W.msm.prob
p$role.B.msmf.prob <- role.B.msmf.prob
p$role.BI.msmf.prob <- role.BI.msmf.prob
p$role.H.msmf.prob <- role.H.msmf.prob
p$role.HI.msmf.prob <- role.HI.msmf.prob
p$role.W.msmf.prob <- role.W.msmf.prob
p$inst.trans.matrix <- matrix(1, nrow = 1)
p$role.trans.matrix <- matrix(c(1, 0, 0,
0, 1, 0,
0, 0, 1),
nrow = 3)
p$riskh.start <- max(1, prep.start - prep.risk.int - 1)
p$method <- method
p$modes <- 1
p$asmr.B.f <- asmr.B.f
p$asmr.BI.f <- asmr.BI.f
p$asmr.H.f <- asmr.H.f
p$asmr.HI.f <- asmr.HI.f
p$asmr.W.f <- asmr.W.f
p$asmr.B.m <- asmr.B.m
p$asmr.BI.m <- asmr.BI.m
p$asmr.H.m <- asmr.H.m
p$asmr.HI.m <- asmr.HI.m
p$asmr.W.m <- asmr.W.m
class(p) <- "param.net"
return(p)
}
#' @title Epidemic Model Initial Conditions for SHAMP
#'
#' @description Sets the initial conditions for a stochastic epidemic models
#' simulated with \code{\link{netsim}}.
#'
#' @param prev.R.S Initial disease prevalence among persons of group R,
#' where R is a charater (B, BI, H, HI or W), and sex group S
#' where s is a charter (f, msf, msm, msmf)..
#' @param ... Additional arguments passed to function.
#'
#' @return
#' A list object of class \code{init_shamp}, which can be passed to EpiModel
#' function \code{\link{netsim}}.
#'
#' @keywords SHAMP
#'
#' @export
init_shamp <- function(prev.B.f = 0.02,
prev.BI.f =0.02,
prev.H.f =0.02,
prev.HI.f =0.02,
prev.W.f = 0.02,
prev.B.msf = 0.02,
prev.BI.msf =0.02,
prev.H.msf =0.02,
prev.HI.msf =0.02,
prev.W.msf = 0.02,
prev.B.msm = 0.02,
prev.BI.msm =0.02,
prev.H.msm =0.02,
prev.HI.msm =0.02,
prev.W.msm = 0.02,
prev.B.msmf = 0.02,
prev.BI.msmf =0.02,
prev.H.msmf =0.02,
prev.HI.msmf =0.02,
prev.W.msmf = 0.02,
...) {
p <- get_args(formal.args = formals(sys.function()),
dot.args = list(...))
p$init.prev.age.slope.B.f <- prev.B.f / 42
p$init.prev.age.slope.BI.f <- prev.BI.f / 42
p$init.prev.age.slope.H.f <- prev.H.f / 42
p$init.prev.age.slope.HI.f <- prev.HI.f / 42
p$init.prev.age.slope.W.f <- prev.W.f / 42
p$init.prev.age.slope.B.msf <- prev.B.msf / 42
p$init.prev.age.slope.BI.msf <- prev.BI.msf / 42
p$init.prev.age.slope.H.msf <- prev.H.msf / 42
p$init.prev.age.slope.HI.msf <- prev.HI.msf / 42
p$init.prev.age.slope.W.msf <- prev.W.msf / 42
p$init.prev.age.slope.B.msm <- prev.B.msm / 42
p$init.prev.age.slope.BI.msm <- prev.BI.msm / 42
p$init.prev.age.slope.H.msm <- prev.H.msm / 42
p$init.prev.age.slope.HI.msm <- prev.HI.msm / 42
p$init.prev.age.slope.W.msm <- prev.W.msm / 42
p$init.prev.age.slope.B.msmf <- prev.B.msmf / 42
p$init.prev.age.slope.BI.msmf <- prev.BI.msmf / 42
p$init.prev.age.slope.H.msmf <- prev.H.msmf / 42
p$init.prev.age.slope.HI.msmf <- prev.HI.msmf / 42
p$init.prev.age.slope.W.msmf <- prev.W.msmf / 42
class(p) <- "init.net"
return(p)
}
#' @title Epidemic Model Control Settings
#'
#' @description Sets the controls for stochastic network models simulated with
#' \code{\link{netsim}}.
#'
#' @param simno Unique ID for the simulation run, used for file naming purposes
#' if used in conjunction with the \code{EpiModelHPC} package.
#' @param nsims Number of simulations.
#' @param ncores Number of cores per run, if parallelization is used within the
#' \code{EpiModelHPC} package.
#' @param nsteps Number of time steps per simulation.
#' @param start Starting time step for simulation, with default to 1 to run new
#' simulation. This may also be set to 1 greater than the final time
#' step of a previous simulation to resume the simulation with different
#' parameters.
#' @param initialize.FUN Module function to use for initialization of the epidemic
#' model.
#' @param aging.FUN Module function for aging.
#' @param deaths.FUN Module function for general and disease-realted deaths.
#' @param births.FUN Module function for births or entries into the population.
#' @param test.FUN Module function for diagnostic disease testing.
#' @param tx.FUN Module function for ART initiation and adherence.
#' @param prep.FUN Module function for PrEP initiation and utilization.
#' @param progress.FUN Module function for HIV disease progression.
#' @param vl.FUN Module function for HIV viral load evolution.
#' @param aiclass.FUN Module function for one-off AI risk class transitions.
#' @param roleclass.FUN Module function for transitions in sexual roles.
#' @param resim_nets.FUN Module function for network resimulation at each time
#' step.
#' @param disclose.FUN Module function for HIV status disclosure.
#' @param acts.FUN Module function to simulate the number of sexual acts within
#' partnerships.
#' @param condoms.FUN Module function to simulate condom use within acts.
#' @param riskhist.FUN Module function to calculate risk history for uninfected
#' persons in the population.
#' @param position.FUN Module function to simulate sexual position within acts.
#' @param trans.FUN Module function to stochastically simulate disease transmission
#' over acts given individual and dyadic attributes.
#' @param heatbath.FUN Module function to stochastically simulate disease aquisition by msmf via
#' contact with an unsimulated msm population.
#' over acts given individual and dyadic attributes.
#' @param immigration.FUN Module function to stochastically simulate disease aquisition by migrants
#' via conatct with unsimulated populations in home counties during return trips.
#' @param prev.FUN Module function to calculate prevalence summary statistics.
#' @param verbose.FUN Module function to print model progress to the console or
#' external text files.
#' @param save.nwstats Calculate and save network statistics as defined in the
#' \code{simnet} modules.
#' @param verbose If \code{TRUE}, print out simulation progress to the console
#' if in interactive mode or text files if in batch mode.
#' @param verbose.int Integer specifying the interval between time steps at which
#' progress is printed.
#' @param ... Additional arguments passed to the function.
#'
#' @return
#' A list object of class \code{control_shamp}, which can be passed to the
#' EpiModel function \code{netsim}.
#'
#' @keywords SHAMP
#'
#' @export
control_shamp <- function(simno = 1,
nsims = 1,
ncores = 1,
nsteps = 100,
start = 1,
initialize.FUN = initialize_shamp,
aging.FUN = aging,
deaths.FUN = deaths_shamp,
births.FUN = births_shamp2,
demogupdate.FUN = demogupdate_shamp,
test.FUN = test_shamp,
tx.FUN = tx_shamp,
prep.FUN = prep_shamp,
progress.FUN = progress_msm,
vl.FUN = vl_shamp,
#aiclass.FUN = NULL,
#roleclass.FUN = NULL,
resim_nets.FUN = simnet_shamp,
disclose.FUN = disclose_shamp,
acts.FUN = acts_shamp,
condoms.FUN = condoms_shamp,
riskhist.FUN = riskhist_shamp,
position.FUN = position_shamp,
trans.FUN = trans_shamp,
heatbath.FUN = heatbath_msmf_shamp,
immigration.FUN = immigration_shamp,
prev.FUN = prevalence_shamp,
verbose.FUN = verbose_msm,
save.nwstats = FALSE,
verbose = TRUE,
verbose.int = 1,
...) {
formal.args <- formals(sys.function())
dot.args <- list(...)
p <- get_args(formal.args, dot.args)
p$skip.check <- TRUE
p$save.transmat <- FALSE
bi.mods <- grep(".FUN", names(formal.args), value = TRUE)
bi.mods <- bi.mods[which(sapply(bi.mods, function(x) !is.null(eval(parse(text = x))),
USE.NAMES = FALSE) == TRUE)]
p$bi.mods <- bi.mods
p$user.mods <- grep(".FUN", names(dot.args), value = TRUE)
p$save.other = c("attr", "temp", "el", "p")
p$save.network = FALSE
class(p) <- "control.net"
return(p)
}
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