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params_uganda_d3.R
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params_uganda_d3.R
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## Add sdp parameters
## 10 July 2014: Add 40%
## 2 July 2014: Vary PMTCT coverage: add 20%
## 1 Jul 2014: Update mean CD4 count for initiation
## to 174 (currently it is 145)
## 26 June 2014: Additions to model ART eligibility at 500:-
## a. Coverage: does not change
## b. Mean CD4 at initiation: 145
## 24 June 2014: Vary PMTCT coverage: 50%, 60%, 70% 80%
## and compute how many would be virally suppressed in
## those cases
## 28 April 2014: Add "OptB.thres" argument --
## see "notes.txt" with this date
## 23 April 2014:
## a. for a below, "eligible.cd4" also needs to be
## changed to 500.
## 22 April 2014:
## a. UG start at CD4 of 500.
## b. hemodilution start for pregnant women at CD4 of 450.
## 14 Mar 2014: Add ZA fertility rates
## 21 Feb 2014:
## a. Set sc.art.postcess.ret.bl= sc.art.postcess.ret.bl.chung
## to avoid confusion later.
## b. Already had Add optb.vl.postcess.ret.bl=2*30/14 ##21Feb14
## c. Differentiate
## optA.sc.art.vl.perstep.dec and optA.sc.art.cd4.perstep.rec with
## optA.sc.art.vl.perstep.dec.idealized and optA.sc.art.cd4.perstep.rec.idealized
## (these idealized "dec" and "ret" params to not have to be used -- see
## email from Sarah dated 21 Feb 2014)
## Also change 23 weeks to 22 weeks in calculations above
## 19 Feb 2014: Add argument in function to
## optb.vl.postcess.ret.bl=2*30/14
## 12 Feb 2014: Add 1 month return post-ART cessation
## 14 Jan 2013: Use Hollingsworth's multiplier
## 22 Dec 2013:
## Add parameter for cessation of Option B ART:
## optB.cess.time = round(12*30/14)
##18Dec13: Added
## idealized.cd4.at.art.initiation.men <- 350
## idealized.cd4.at.art.initiation.women <- 350
## for interventions
## 4 Dec 2013:
## a. Differentiate prevalence at recruitment for men and women -- 1.9% for men,
## 4.9% for women; earlier, I was using an average recruit prevalence of 3% for everyone
## b. Decided on 75% adherence for PMTCT under idealized intervention, consistent
## with adherence for realistics PMTCT intervention. For regular ART, adherence estimate
## under realistic and idealized interventions are also the same. The realistic
## coverage rate for PMTCT already accounts for adherence.
## 1 Dec 2013: Also adjust "idealized.art.coverage.rate" for adherence using
## multiplicative model -- similar to adjustment for
## "baseline.art.coverage.rate"
## 25 Nov 2013:
## Adjust "baseline.art.coverage.rate" for adherence. We will do this by using
## a multiplicative model for coverage and adherence. See email dated 22 Nov 2013
## from Sarah
## 18 Nov 2013:
## a. Adjust age distribution here now that individuals enter at age 18.
## b. Make sure "preg.susc.mnult" in transmission function and "preg.mult"
## in
## 16 Nov 2013: need to make change to burnin and intervention simulation,
## make sure "assign.infectivity" function has argument "preg.mult" and
## "transmission" function has argument "preg.susc.mult."
## 8 Nov 2013: Change name of data "dur.inf.by.age" to "given.dur.inf.by.age"
## to avoid confusion with the attribute which appears later
## 7 Nov 2013: Change min.age to 18.
## 28 Oct 2013: Add idealized intervention parameters here, so they
## don't have to be called separately.
## 23 Oct 2013:
## a. add the pregnancy parameters: "full.term", "optA.vl.reduction" and
## "sc.art.postcess.rest.bl" here
## b. Add "preg.susc.mult" to differentiate multiplier for susceptible women
## 1 Oct 2013: add partnership duration restricted to 15 to 55.
## 26 Sep 2013: Increase number of births per 1000 women by
## 15%
## 3 Sep 2013: Revise fertility rates (per 1000) based on Sarah's
## email dated 3 Sep 2013.
## 2 Sep 2013: Add attributes for stratifying pregnancy by age
## and infection status.
## 26 Aug 2013: Max Survival for everyone -- add that NOW!!!!
## 22 Aug 2013:
## a. Add age-based expected life expectancy at time of infection
## b. Realistic CD4 initiation 131 (100 for South Africa)
## 20 Aug 2013: Change initial prevalence to 10%.
## 15 Aug 2013: I think min.chronic.infectivity is off by an order of magnitude.
### File to maintain comprehensive list of parameters
## Basic Population Set UP
num.male <- 2500
num.female <- 2500
N <- num.male+num.female
## BEHAVIOR
## Network Related Statistics
## Degree Distribution, Partnership Duration
## and Number of Partnerships
male.deg.dist <- c(34.8, 52.3, 9.8, 3.1)/100 ## Phase 2 data
female.deg.dist <- c(42.0, 55.7, 2.3, 0)/100 ## Phase 2 data
size.of.timestep <- 14 ## each time step is 14 days
duration <- 4303/size.of.timestep
duration.15to55 <- 4139/size.of.timestep #1Oct2013
diagnostics <- T
female.deg.counts <- female.deg.dist*num.female
male.deg.counts <- male.deg.dist*num.male
female.deg.tot <- (0*female.deg.counts[1] + 1*female.deg.counts[2] +
2*female.deg.counts[3] + 3*female.deg.counts[4])
male.deg.tot <- (0*male.deg.counts[1] + 1*male.deg.counts[2] +
2*male.deg.counts[3] + 3*male.deg.counts[4])
## to match the degree totals for men and women, reduce the number of
## isolates in the women, and increase the number of women with 1 partner
female.deg.dist.matched <- c(22.0, 75.7, 2.3, 0)/100
female.deg.counts.matched <- female.deg.dist.matched*num.female
female.deg.tot.matched <- (0*female.deg.counts.matched[1] +
1*female.deg.counts.matched[2] +
2*female.deg.counts.matched[3] +
3*female.deg.counts.matched[4])
## DEMOGRAPHIC ATTRIBUTES
max.survival <- 55 # 26 Aug 2013 -- max age (in years)
## Sex, age, circumcision status,
## pregnancy status,
## other pregnany related parameters
## Age (in accordance with proportions from census data)
##min.age <- 15
min.age <- 18
max.age <- 54
## age.distribution <- c(0.25468095,
## 0.20238352,
## 0.155598745,
## 0.119586275,
## 0.095660855,
## 0.07367249,
## 0.058677315,
## 0.04014263)
age.distribution <- c(0.12,
0.24,
0.18,
0.14,
0.11,
0.09,
0.07,
0.05)
age.classes <- c(#seq(15, 19, by=1),
seq(18, 19, by=1), #7Nov13
seq(20, 24, by=1),
seq(25, 29, by=1),
seq(30, 34, by=1),
seq(35, 39, by=1),
seq(40, 44, by=1),
seq(45, 49, by=1),
seq(50, 54, by=1)
) # create a vector of all ages of interest
## circumcision status
circum.rate <- 96/(851+96) ## No 851; Yes 96; Refusal 1; NA 0,
## see file "data_report.pdf"
## num.births.per1k.byage =
## round(c(83.2206172368,
## 154.3021435787,
## 167.5508974253,
## 122.3312442802,
## 76.2926740922,
## 29.3483793413,
## 9.424565988), 1) #2Sep13: CHECK!!!!!!!!!
num.births.per1k.byage = round(c(
174.967,
345.9442,
320.1459,
266.055,
184.28,
79.91913,
36.50134),
1) # for 1 decimal place
## prop.stillbirth = 0.10 #2Sep13
prop.stillbirth = 24.8/1000 #2Sep13
inf.preg.red = 0.53 #2Sep13
num.births.per1k.byage.15pcinc <- num.births.per1k.byage+
(0.15*num.births.per1k.byage)
## 14Mar14: South Africa fertility rates for
## counterfactual analysis
num.births.per1k.byage.za = round(c(
c(80.6,
139,
141.8,
105.6,
67.4,
27.1,
8.8)),
1) # for 1 decimal place
prop.stillbirth.za = 20.4/1000 #2Sep13
num.births.per1k.byage.15pcinc.za <- num.births.per1k.byage.za+
(0.15*num.births.per1k.byage.za)
## BIOLOGICAL ATTRIBUTES
## Infection Status
## init.hiv.prev <- 0.06 # set to about 6% for Uganda
## init.hiv.prev <- 0.10 # set to about 6% for Uganda
## init.hiv.prev <- 0.15 # 22Aug2013
init.hiv.prev <- 0.10 # 23 Aug 2013
init.hiv.prev.6 <- 0.06 # 23 Aug 2013
init.hiv.prev.15 <- 0.15
init.hiv.prev.10 <- 0.10 # 3Sep13
recruit.inf.prop.male <- 1.9/100 #4Dec13
recruit.inf.prop.female <- 4.9/100 #4Dec13
## Time Since Infection
duration.of.infection <- 3300 ## in days, modify later
## infectivity for infected individuals
# min.chronic.infectivity <- 0.00497/2.89
#min.chronic.infectivity.unadj <- 0.00497/2.89
# changed to include infection at log 2
min.chronic.infectivity.unadj <- 0.000497/2.89 # 15 Aug 2013: was off by order. of
# magnitude
## Time of Infection
acute.length <- 1:floor(121/size.of.timestep) ## in daily time units
chronic.length <- ceiling(121/size.of.timestep):floor(1877/size.of.timestep)
late.length <- ceiling(1877/size.of.timestep):floor(3300/size.of.timestep)
## CD4 Counts
## Set to 518 for men and 570 for women
## For positives, this will change as we step through time loop.
## Relevant parameters
cd4.at.infection.male <- 518 #cells/mm3
cd4.at.infection.female <- 570 #cells/mm3
untreated.cd4.daily.decline <- 0.14 # (for men and women)
untreated.cd4.perstep.decline <- untreated.cd4.daily.decline*size.of.timestep
untreated.cd4.time.to.350.men <- 3.3*365/size.of.timestep # changed due to timestep
untreated.cd4.time.to.350.men <- 4.2*365/size.of.timestep # changed due to timestep
## Viral Load Today
## List viral load parameters, adjusted for size of timestep
time.infection.to.peak.viremia <- floor(14/size.of.timestep)
time.infection.to.peak.viral.load <- time.infection.to.peak.viremia
peak.viral.load <- 6.17
time.infection.to.viral.set.point <- floor(121/size.of.timestep)
set.point.viral.load <- 4.2
time.infection.to.late.stage <- floor(1877/size.of.timestep)
dur.inf <- floor(3300/size.of.timestep)
late.stage.viral.load <- 5.05 ## (max?)
time.infection.to.peak.viral.load
time.to.full.supp <- 4*30/size.of.timestep ## 4 months
undetectable.vl <- log(50, base=10)
## dur.inf.by.age <- round(c(12.8*365/size.of.timestep,
## 10.6*365/size.of.timestep,
## 7.5*365/size.of.timestep,
## 5.6*365/size.of.timestep))
given.dur.inf.by.age <- round(c(12.8*365/size.of.timestep,
10.6*365/size.of.timestep,
7.5*365/size.of.timestep,
5.6*365/size.of.timestep))
## ART
#baseline.art.coverage.rate <- 0.40 # coverage
baseline.art.coverage.rate <- 0.487 #25Nov13
art.adherence.rate <- 0.885 #25Nov13
baseline.art.coverage.rate <- baseline.art.coverage.rate*art.adherence.rate #25Nov13
baseline.preg.art.coverage.rate <- baseline.preg.coverage.rate <- 0.43 #14Oct13
# coverage # CHECK THIS
idealized.art.coverage.rate <- 0.90 # 28Oct13
idealized.art.coverage.rate <- idealized.art.coverage.rate*art.adherence.rate
# 1 Dec 2013
idealized.preg.coverage.rate <- 0.90 # 28Oct13
preg.adherence.rate <- 0.75 #4Dec13
idealized.preg.coverage.rate <- idealized.preg.coverage.rate*preg.adherence.rate
#4Dec13
idealized.preg.coverage.rate.50 <- 0.50 #24Jun14
idealized.preg.coverage.rate.60 <- 0.60 #24Jun14
idealized.preg.coverage.rate.70 <- 0.70 #24Jun14
idealized.preg.coverage.rate.80 <- 0.80 #24Jun14
idealized.preg.coverage.rate.20 <- 0.20 #2Jul14
idealized.preg.coverage.rate.40 <- 0.40 #2Jul14
cd4.recovery.time <- 3*365/size.of.timestep ## CD4 recovery for 3 years
per.day.cd4.recovery <- 15/30 ## rate of 15 cells/month
eligible.cd4 <- 350
eligible.cd4.500 <- 500 #23Apr14
baseline.cd4.at.art.initiation.men <- 131 # 22Aug13: customized for UG
baseline.cd4.at.art.initiation.women <- 131 # 22Aug13: customized for UG
baseline.cd4.at.art.initiation.men.artelig500 <- 174 #1Jul14. Previously 145 (#26Jun14)
baseline.cd4.at.art.initiation.women.artelig500 <- 174 #1Jul14. previously 145 (#26Jun14)
idealized.cd4.at.art.initiation.men <- 350
idealized.cd4.at.art.initiation.women <- 350
#18Dec13:added for interventions
cd4.at.art.initiation.men.500 <- 500 #22Apr2014
cd4.at.art.initiation.women.500 <- 500 #22Apr2014
cd4.at.art.initiation.women.450 <- 450 #22Apr2014
bl.min.art.init.timestep.male <- (cd4.at.infection.male -
baseline.cd4.at.art.initiation.men)/
untreated.cd4.perstep.decline
bl.min.art.init.timestep.female <- (cd4.at.infection.female -
baseline.cd4.at.art.initiation.women)/
untreated.cd4.perstep.decline
idealized.cd4.at.art.initiation.men <- 350
idealized.cd4.at.art.initiation.women <- 350
## Option A
optA.sc.art.vl.perstep.dec <- 1.1/((40-22)*7)*size.of.timestep
## decline is 1.1 log over 17 weeks(from first visit to delivery)
## Per day decline, therefore, is 1.1/((40-23)*7)
## Per time step decline, therefore, is given by expr. above
optA.sc.art.cd4.perstep.rec <- 50/((40-22)*7)*size.of.timestep
## recovery is 50 cells/mm3 over 17 weeks
## (from first visit to delivery)
## same logic as above applies
optA.sc.art.vl.perstep.dec.idealized <- 1.1/((40-14)*7)*size.of.timestep #21Feb14
optA.sc.art.cd4.perstep.rec.idealized <- 50/((40-14)*7)*size.of.timestep #21Feb14
optA.thres=350
optB.thres=350#28Apr14
## Option B
optB.cess.time = round(12*30/14)
## Demographic Parameters
## Mortality
## 7Jul13: Adjusted to realistic values
asmr.perperson.perday <- c(6.87671232876712E-006,
1.31232876712329E-005,
1.93424657534247E-005,
2.66027397260274E-005,
3.7013698630137E-005,
4.59452054794521E-005,
5.29315068493151E-005,
5.68493150684932E-005
)
asmr.perperson.pertimestep <- asmr.perperson.perday*size.of.timestep
asmr.male <- asmr.perperson.pertimestep
asmr.female <- asmr.perperson.pertimestep
## Births
phi <- 0.001*5 ## mean parameter for poisson process
phi <- (phi/5)*3 #29Aug13, 2Sep13
phi.std <- 0.001*1
phi.std2 <- 0.001*2
phi.zero <- 0 #22 Aug 2013
phi.std5 <- 0.001*5
phi.std4 <- 0.001*4
phi.std45 <- 0.001*4.5
phi.std35 <- 0.001*3.5
## Pregnancy
full.term=40/14*7
min.preg.interval=15*30/14
optA.vl.reduction=1.1
## sc.art.postcess.ret.bl=6*30/14 ## return in 6 months = 180/14 timesteps
baseline.f.ges.visit=23*7/14
idealized.f.ges.visit=14*7/14
sc.art.postcess.ret.bl.chung=1*30/14 ## return in 1 month = 30/14 timesteps
sc.art.postcess.ret.bl <- sc.art.postcess.ret.bl.chung #21Feb14
optB.vl.postcess.ret.bl=2*30/14 ##19Feb14
## Transmission Parameters
## Frequency of Sex
num.sex.acts.per.timestep <- 2.4*size.of.timestep/7
acute.mult <- 4.98
late.mult <- 3.49
preg.mult <- 2.5 ## check
circum.mult <- 0.60 ## check
preg.susc.mult <- 1.7
acute.mult.holling <- 26
late.mult.holling <- 7
## New parameters for SDP
hbhtc.testing.coverage=0.80
known.sdp.art.coverage=58.4/100
known.sdp.art.at.cd4=350
not.known.sdp.art.coverage=58.4/100
not.known.sdp.art.at.cd4=350
decline.ui=0.63
known.sdp.art.coverage.high=0.90
known.sdp.art.at.cd4.high=1e3