R/input_parameters_primary.R
In EvoNetHIV/Test_and_Treat: Evonet: Integrated Bio-Social Models for HIV Epidemiology
Defines functions
input_parameters_primary_DEPRECATED
#NOTE!
# This function is now deprecated.
# When called, fxn "input_params" will be called instead
# So, put all new parameters in the "input_params" fxn argument list
#
input_parameters_primary_DEPRECATED <-function(){
#NOTE!
# This function is now deprecated.
# When called, fxn "input_params" will be called instead
# So, put all new parameters in the "input_params" fxn argument list
#
list(
#-- Basic model setup parameters -------------#
model_name = "evomodel",
hpc = FALSE, #on hyak?
output_path = getwd(),
start_timestep = 1, #parameter for EpiModel, should be "1" if new simulation
#if re-starting simulation,
#value should be "n_steps+1" (n_steps from original sim.)
# new parameters for alternative restart routines
restart_val = "none", # "save" = save simulation, "restart" = restart simulation
restart_time = 1e9, # time that base simulation results get saved
restart_tx_type = "CD4_low",
fast_edgelist = FALSE,
nsims = 1,
initial_pop = 100, #initial popn
n_steps = 365*2,
initial_infected = 20,
model_sex = "msm",
scrolling_output = TRUE,
print_frequency = 10, # Set to 10 to print to output screen every 10 days. Default should be 1.
plot_nw = TRUE,
plot_mean_degree_by_age = FALSE, # Set to true to see plots of mean degree broken out by age.
network_print_frequency = 100,
save_network = FALSE,
save_coital_acts = FALSE,
save_vl_list = FALSE, #TRUE to graph individual agent VL
save_infection_matrix=FALSE,
popsumm_frequency=1, #frequency of timesteps should popsumm stats be calculated
estimate_new_nw = TRUE, #for "master_script_loop", if false, only 1 nw estimated
ncores =1, #16 if running on hyak using EpiModelHPC
save_partner_list = FALSE,
#--QA/QC terms ----------------------------#
QA_QC_pause_time = 3, # Time delay in seconds that the computer waits so that users can view QA/QC stats
#-- Network estimation terms -------------#
modes = 1, #epimodel param, will change to 2 in "input_params_derived" if hetero model
nw_form_terms = "~edges + offset(nodematch('role', diff=TRUE, keep=1:2))",
nw_coef_form = c(-Inf, -Inf),
target_stats = 100*0.7/2,
relation_dur = 50,
nw_constraints = " ~.",
dissolution="~offset(edges)",
rm_offset_rel = F, # temporary solution to remove same-role (MSM simulations) and same-sex (heterosexual simulations) relationships. If = T, function remove_offset_relationships will be called in initialize_module.
#-- virulence/vl/transmission model parameters -------------#
VL_Function = "aim2", # Other option is "aim3" (aim 3 code)
vl_peak_agent_flag = FALSE,
transmission_model = "hughes",
max_spvl_allowed = 7.0,
min_spvl_allowed = 2.0,
max_time_inf_initial_pop = 365*2,
vl_full_supp = 13.0, # final viral load after complete treatment
vl_undetectable = 50.0,
vl_exp_decline_tx = -0.6,
InfRateBaseline = 0.0000003, # 0.0001178/365 # Lingappa 2010
InfRateExponent = 3.52, # Lingappa 2010
MaxInfRate = 0.002, # Asymptotic function from Fraser 2007, Assuming P_inf(1 year) = 1 - (1 _ P_inf(1 day))^365
VHalfMaxInfRate = 13938, # Fraser 2007
HillCoeffInfRate = 1.02, # Fraser 2007
shape_parameter = 3.46, # 3.46 is from Fraser
Dmax = 9271, # from Fraser, maximum time in days of asymptomatic state
D50 = 3058, # from Fraser, spVL at which duration is half maximum
Dk = 0.41, # 0.41 is from Fraser, Hill coefficient
V0 = 1e-4,
vl_peak_acute = 7.7e6, # Average viral load during primary infection; from Little et al 1999
vl_max_aids = 2.4e6, # Piatak 1993
vl_increase_AIDS = 1.0041122, #determines slope of vl increase after aids onset; defaault value implies 400-fold increase over 4 years. E.g. O’Brien and Hendrickson 2013.
t_peak = 21.0,
t_acute = 90,
t_acute_phase2 = 32, # Changed on 3/29/16 to give more realistic acute phase dynamics with the revised acute phase code
acute_decline_phase2_rate = -.03,
vl_decay_rate_phase2 = 0.02, #new on 3/2/16, revised acute decline code
AverageLogSP0 = 4.5,
VarianceLogSP0 = 0.8,
MaxPPP = 0.0, # Maximum per pathogen pathogenecity (PPP). Viruses w/ PPP's > 0.0 kill hosts faster for a given SPVL
MutationVariance = 0.01,
max_vl_viralcont_spvl = 7,
min_vl_viralcont_spvl = 2.5,
prog_rate = 0.14, # per year rate. Note VL progression cartoons typically show ~0.5 Log increase in VL over a 8-year
# period. That suggests a progression rate of ln(10^0.5) / 8 = ~0.14 per year
Heritability = 0.36, # desired population heritability estimate is 0.36 (from Fraser; should vary by population). This value, in conjunction with the default mutation variance, seems to yield a stable population heritability of 3.6. But user should always beware!
Flat_Viral_Load = 0, # Setting this to 1 forces VL to be the same value during the entire primary infection period
trans_lambda = 0.000247, # From Steve Goodreau's summary of discussions he had with Jim Hughes
trans_lambda_init = 0.003,
trans_lambda_alpha = 5,
trans_RR_uses_condoms= 0.22, # From Table 4 from Hughes et al. (2012, JID)
trans_RR_LogV = 2.89, # From Table 4 from Hughes et al. (2012, JID)
trans_VLbase = 4.0, # From Steve Goodreau's summary of discussions he had with Jim Hughes
trans_RR_circumcised = 0.53, # From Table 4 from Hughes et al. (2012, JID)
trans_RR_age = 0.67, # Older people have lower risk (Note: 0.82 in the publication. 0.67 comes from Steve after talking to Jim Hughes) [Note that Table 3 from Hughes et al. 2012 gives 0.82]
max_age_RR_age = 45, #threshold age, where ages above threshold have same RR
trans_base_age = 35, # From Steve Goodreau's summary of discussions he had with Jim Hughes
trans_RR_STI = 2.7, # placeholder for generic sti
trans_RR_insertive_anal_msm = 2.9, #From Patel 2014, but adjusted for circumcision. See Evonet params.xls and V-A transmission ratios.xlsx. Was 5 until 7/6/2016
trans_RR_receptive_anal_msm = 17.3, #From Patel 2014, but adjusted for circumcision. See Evonet params.xls and V-A transmission ratios.xlsx. Was 10 until 7/6/2016
trans_RR_ins_ai = 2.9,
trans_RR_rec_ai = 17.3,
trans_RR_acute_phase = 1.0, #increased infectiousness during acute phase
trans_RR_receptive_vaginal=1,
trans_RR_vaccine = 0.01, #placeholder, 5/3/16
perc_virus_vaccine_sens = 0.99, #placeholder 5/3/16
# Parameters for newly added (11/8/15) dynamic CD4 function (revised 11/11/15)
cd4_homo_input = 0.04, # Rate of addition of CD4 T-cells per day when CD4 < 1000
k_cd4 = 0.127, # Rate at which virus kills CD4 T-cells [using log10(virus)]
vl_kill_cd4 = 0.38, # VL below which virus no longer kills off CD4 T-cells
min_prop_blood = 0.5, # Proportion of CD4 T-cells that stay in blood no matter how high VL gets
V_half_redist = 4e5, # Viral load at which 50% of mobilizable CD4 T-cells migrate to lymph nodes
CD4count_end_stage = 1, # CD4 count at which patients die of AIDS
# -- Viral Load / Virulence parameters specific for Aim 3 modeling
# Note with changes made 8/31/15: V_peak, V_SPVL, and V_AIDS are now defined by Aim 2 values given above.
Save_VL_Histories = FALSE,
Max_Allowable_Loci = 5,
prob_CYP_6_slow = 0,
step_size_C = 0.0004, # Step size in C program simulator
s_CD4 = 10, # Rate of input of CD4 T-cells from the thymus
m_CD4 = 0.01, # Natural death rate of CD4 T-cells
k = 0.0000001, # Rate at which viruses kill off CD4 T-cells
r_inf_cells = 1.8, # growth rate of infected cells at start of infection
r_r_inf_cells = 1.3, # growth rate of drug resistant infected cells at start of infection
d_inf_cells = 0.6, # death rate of infected cells
f_M = 0.02, # Fraction of target cells that become moderately long-lived infected cells upon infection
f_L = 1e-6, # Fraction of target cells that become very long-lived (latently) infected cells upon infection
d_M = 0.04, # Death rate of moderately long-lived infected cells
d_L = 0.001, # Death rate of latently infected cells
p_inf_cells = 1000, # Rate at which actively infected cells produce virus
p_M = 100, # Rate at which moderately long-lived infected cells produce virus
p_L = 10, # Rate at which latently infected cells produce virus
M_act = 0.01, # Rate at which M cells convert into productively infected cells, I
L_act = 0.0005, # Rate at which latently infected cells convert into productively infected cells
c = 50, # Clearance rate of free virus (Initial rate prior to virus damaging the immune system)
mu = 3e-5, # Rate at which viruses mutate from being sensitive to being resistant
TransBottleneck = 3e8, # Reduction in viral load associated with transmission
no_loci = 5, # Number of loci conferring drug resistance
cost1 = 0.05, # Fitness cost of having a drug resistance mutation at locus 1
cost2 = 0.05, # Fitness cost of having a drug resistance mutation at locus 2
cost3 = 0.05, # Fitness cost of having a drug resistance mutation at locus 3
cost4 = 0.05, # Fitness cost of having a drug resistance mutation at locus 4
cost5 = 0.05, # Fitness cost of having a drug resistance mutation at locus 5 (note: this mut negates cost at locus 2)
cost_reduct4on2 = 0.9, # Extent to which mutation 4 mitigates the cost of mutation 2
cost_reduct5on1 = 0.5, # Extent to which mutation 5 mitigates the cost of mutation 1
additive_fitness = 0, # 1: fitness = 1 - cost1 - cost2 -... - cost5. <>1: multiplicative: Fitness = (1-cost1)*(1-cost2)*...(1-cost5)
DrugDose1 = 200, DrugDose2 = 200, DrugDose3 = 200.0, DrugDose4 = 200.0, # Dose of drugs taken
drug_decay1 = 1.0, # Per day clearance rate of drug 1
drug_decay2 = 1.0, # Per day clearance rate of drug 2
drug_decay3 = 1.0, # Per day clearance rate of drug 3
drug_decay4 = 1.0, # Per day clearance rate of drug 3
drug_2nd_decay1 = 0.1, # Second-phase decay rate drug 1
drug_2nd_decay2 = 0.1, # Second-phase decay rate drug 2
drug_2nd_decay3 = 0.1, # Second-phase decay rate drug 3
drug_2nd_decay4 = 0.1, # Second-phase decay rate drug 4
conc_2nd_phase1 = 1.0e-70, # Drug concentration at which second-phase starts for drug 1
conc_2nd_phase2 = 1.0e-70, # Drug concentration at which second-phase starts for drug 2
conc_2nd_phase3 = 1.0e-70, # Drug concentration at which second-phase starts for drug 3
conc_2nd_phase4 = 1.0e-70, # Drug concentration at which second-phase starts for drug 4
min_adherence1 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence1 = 1,
min_adherence2 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence2 = 1,
min_adherence3 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence3 = 1,
min_adherence4 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence4 = 1,
adherence_type = 1:2, #1=random, 2=cyclic
adherence_type_prob=c(1,0),#default: all agents adherence type 1,
adherence_days_high=5,#for cyclic adherence
adherence_days_low=2,#for cyclic adherence
aherence_days_high_prob=0.9,#for cyclic adherence
aherence_days_low_prob=0.1,#for cyclic adherence
BaseIC50Drug1 = 200.0, BaseIC50Drug2 = 200.0, BaseIC50Drug3 = 200.0, # Concentration of drug that blocks V by 50%
BaseIC50Drug4 = 2.0, # Drug 4 is some super-effective 2nd-line therapy combo
Interaction_Model_Drugs12 = 1, # 1 = Bliss independence, 2 = Simple saturation (Huang et al. 2003),
# 3 = Lowe additivity (not yet implemented)
# Background: Current parameterizations assumes that drugs 1 and 2 are both NRTIs
# Therefore, they may compete for the active site, reducing the degree of inhibition
# Interaction_Model_Drugs12 implements different math ideas for how they may combine
FC_D1_Mut1 = 50.0, # Effect of mutation 1 on the IC50 value of drug 1. (Fold-change from baseline)
FC_D1_Mut2 = 1.0,
FC_D1_Mut3 = 1.0,
FC_D1_Mut4 = 1.0,
FC_D1_Mut5 = 1.0,
FC_D2_Mut1 = 1.0,
FC_D2_Mut2 = 50.0,
FC_D2_Mut3 = 1.0,
FC_D2_Mut4 = 1.0,
FC_D2_Mut5 = 1.0,
FC_D3_Mut1 = 1.0,
FC_D3_Mut2 = 1.0,
FC_D3_Mut3 = 10.0,
FC_D3_Mut4 = 5.0,
FC_D3_Mut5 = 1.0,
FC_D4_Mut1 = 1.0,
FC_D4_Mut2 = 1.0,
FC_D4_Mut3 = 1.0,
FC_D4_Mut4 = 1.0,
FC_D4_Mut5 = 1.0,
StochasticCut = 1.0e-6, # Density of cells (or viruses) below which cell changes occur stochastically
AbsoluteCut = 1.0e-7, # Density of cells (or viruses) that correspond to 1 cell per body.
Dosing_Interval =1,#Dosing_Interval =1 means once daily dosing,Dosing_Interval = 2 means twice daily dosing
Therapy_Type = 1,#Therapy_Type = 1 eans three individual pills Therapy_Type = 2 means that drugs 1 and 2 are contained within a single pill, Therapy_Type = 3 means that all three drugs are contained within a single pill
# Stockout parameters (e.g., no gets Drug 1 btw StopDrug1 and RestartDrug1)
# These apply to all patients regardless of their adherence
StopDrug1 = 1000000000.0, # Stock out time (in days) for drug 1
RestartDrug1 = 1000000000.0, # Drug 1 becomes available again
StopDrug2 = 1000000000.0, # Stock out time (in days) for drug 2
RestartDrug2 = 1000000000.0, # Drug 2 becomes available once again
StopDrug3 = 1000000000.0, # Stock out (in days) for drug 3
RestartDrug3 = 1000000000.0, # Drug 3 becomes available once again
StopDrug4 = 1000000000.0, # Stock out (in days) for drug 3
RestartDrug4 = 1000000000.0, # Drug 3 becomes available once again
# -- Parameters related to viral load testing
prob_elig_vl_test = 0.75,
time_on_tx_for_vl_testing = 365/2,
vl_testing_interval = 365/2,
# -- Parameters related to PrEP / Vaccine models that use alleles to model resistance
PrEP_Model = FALSE,
# -- Parameters related to drug resistance testing and start of 2nd line therapy (aim 3 only)
resist_testing_model = "interval",
mean_resist_test_interval = 182,
time_on_tx_for_resist_testing = 182, # Days on tx before clinicians test for resistance
VL_thres_resist_testing = 1e3, # Viral load at which clinicians will do a resistance test
no_muts_switch_2nd_line = 1, # Num resist mutations needed to trigger switch to 2nd-line therapy
#-- vital dyamics model parameters -------------#
min_age = 18,
max_age = 55,
age_dist_new_adds = "min_age", # "mixed" (some min_age, others older)
#or "linear_decline_18_55"
prop_new_agents_min_age = 0.45, #for "mixed" see above line
asmr_data_male = "usa_men_18_to_100",#other option: "south_africa_male"
asmr_data_female = "south_africa_female",
initial_agedata_male = "usa_men_18_to_100", #other options:"south_africa_male_16_to_100", "stable_age_no_hiv_dist"
initial_agedata_female = "south_africa_female_16_to_100_2014", #other options: "stable_age_no_hiv_dist"
aids_death_model = "cd4", # c("Gamma_Death","daily_prob","cd4")
death_rate_constant = 0.000003, # 0.000003 is from CASCADE
death_rate_exponent = 6.45, # 6.45 is from CASCADE
cd4_cat1_death_prob = 0.0000112, #prob. of death for cd4 cat1
cd4_cat2_death_prob = 0.0000148, #prob of death for cd4 cat2
cd4_cat3_death_prob = 0.0000333, #prob of death for cd4 cat3
cd4_cat4_treated_death_prob = 0.0000760, #prob death for cd4 cat4(aids) on tx
cd4_prob_incr_nadir = 0.03, #prob of improving one cd4 cat from nadir
cd4_prob_incr_nadir_minus= 0.0005, #prob of improving one cd4 cat from nadir -1
time_in_aids = 475,
birth_model = "poisson_birth_numbers", # "births=deaths", "poisson_birth_numbers", "exponential_growth","constant_rate", "constant_number", "exponential_growth"
baseline_input_exp_growth = 0.007, # Used with "exponential_growth" MUST BE SCALED BY HAND TO GET A STABLE AGE DISTRIBUTIONbirth_model = "poisson_birth_numbers", # "births=deaths", "poisson_birth_numbers", "constant_rate", "constant_number"
contstant_birth_number = 0,
constant_birth_rate = 0.0001306,
poisson_birth_lambda_base = 0.01370, #scaled to init pop size in 'input_derived_parameters', 7/720/16
pop_growth_rate_annual = 0.01, # as proportion, x100 for percent
constant_rate_spread_out = .01, #birth model: "constant_rate_spread_out"
births_per_year = 1, #birth model: "constant_number_spread_out"
#-- social / treatment /testing parameters -------------#
testing_model = "interval",
no_past_partners_time_prep = 365,
min_past_partners_prep = 1,
min_current_partners_prep = 1,
min_pos_partner_duration = 50,
prep_recent_test = 183,
percent_eligible_on_prep = 1,
start_prep_campaign = 5e5,
prob_tx_droput = 0,
percent_condom_users= 1, # non-users never use condoms when partnered with other non-users. users will use condoms with some probability
compact_el_divisor=1e5,
ave_rel_dur_start = 5*365,
condom_use_rel_dur = FALSE,
condom_use_age = FALSE,
age_condom_use_halves = 50, # Only used when condom_use_age is true
mean_test_interval_male = 365,
mean_test_interval_female = 442,
mean_test_interval_under25 = 365,
test_result_delay = 35, # Delay between being infected and having sufficient antibodies for diagnosis
reduction_test_interval_enhanced = 0.1, # fage reduction in test interval for those identified for "enhanced" testing
prob_enhanced_testing_before_campaign = 0.3, # Percentage of population that gets tested more frequently after scale-up campaign
prob_enhanced_testing_after_campaign = 0.7, # Percentage of population that gets tested more frequently after scale-up campaign
testing_limit = "percent_agents", # For models in which there is an upper limit to number tested. Other is "percent_agents_minus_diagnosed"
tx_type = NA,
mean_trtmnt_delay = 1,
mean_time_tx = 0, # used when tx_type = "time_dist"
sd_time_tx = 0, # used when tx_type = "time_dist"
start_treatment_campaign = 5e5,
tx_limit = "absolute_num", # used with social_treatment_module_multiple_criteria.
max_num_treated = 5e5,
max_num_treated_begin = 5e5, # Num during pre-campaign, used with social_treatment_module_multiple_criteria
max_num_treated = 5e5, # Num during big campaign, used with social_treatment_module_multiple_criteria
proportion_treated = 1,
yearly_incr_tx = 0, # Setting this to 0.1 would mean 10% more people get treated each year
proportion_treated_begin = 0.0,
start_treat_before_big_campaign = 5e5,
num_randomly_chosen_start_campaign = 0, # Number of people outside the prioritized group who get treated (this is actually an outcome rather than a parameter)
num_treated_start_campaign = 0, # Total number of people treated at the start of the campaign (this is actually an outcome rather than a parameter)
num_enhanced_start_campaign = 0, # number diagnosed or receiving enhanced testing (this is actually a variable)
# Below four parameters are used in social_treatment_sex_age module
cov_prob <- c(0.0, 0.01, 0.021, 0.030, 0.049, 0.100, 0.191, 0.283, 0.402, 0.78), # Coverage levels corresponding to years below
cov_prob_yrs <- c(0, 11:18, 23), # Years at which coverage level changes
cov_prob_scal <- matrix(c(0.7242, 0.9955, 1.2593, 0.5895, 0.8103, 1.0251), ncol = 2, dimnames = list(rep("", 3), c("f", "m"))), # values are by sex, then by age within sex
cov_prob_ageg <- list(c(15, 25), c(25, 35), c(35, 56)),
# -- additional treatment parameters --- #
under_25_flag = FALSE,
tx_in_acute_phase = FALSE,
tx_schedule_props =c("F"=1,"V"=0,"N"=0,"P"=0),
treatment_threshold = 1e4, #VL raw,not log10 transformed
cd4_treatment_threshold = 0,
cd4_trt_guidelines_chgs = NA, # Default for SA eligibility changes: list(4, 3:4, 2:4, 1:4)
attr_treatment_threshold = 5, # Under development. When finished, this parameter will
# allow one to target therapy to attribute groups
# "attr_treatment_threshold" and higher (e.g. target therapy
# to people with the most connnections)
min_inf_time_for_treat = 0, # number of timesteps (days)
treat_thresh_partners = 0,
max_treated = NA, #relevant for "social_treatment_john" module,
#this value is set within module at start of tx campaign,
#NA value is flat that indicates it hasn't been initialized yet
prob_care = 1.0, # Probability of being eligible for care before the "under care" campaign
prob_eligible_ART = 1.0,
prob_eligible_2nd_line_ART = 1.0,
second_line_elig = "vl_only",
# -- targeted efforts to get more people under care after "scale_up" compaign --------#
start_scale_up_campaign = 5e5, # At this time, increase the number the number of people eligible for care
prob_care_before_campaign = 1.0, # Probability of being eligible for care after the "under care" campaign (should be >= prob_care)
prob_care_after_campaign = 1.0, # Probability of being eligible for care after the "under care" campaign (should be >= prob_care)
scale_up_type = "random", # Method for prioritizing people for "under care" campaign. See the new routine
# "social_bring_new_groups_into_care" for list of other options (e.g., "AIDS", "under30")
min_age_recruit_for_care = 0.0, # Parameter that allows targeting of people of intermediate ages to get under care
max_age_recruit_for_care = 100, # Parameter that allows targeting of people of intermediate ages to get under care
# -- vaccine parameters --------#
start_vacc_campaign = 5e5,
perc_vaccinated = 0.5,
target_vacc_att = FALSE,
vacc_eff_duration = 365*3,
risk_comp_cond = F, # Set to T to induce reduction in condom use among vaccinated susceptibles and vaccinated, infected, undiagnosed individuals.
risk_comp_cond_rr = 0.70,
risk_comp_degree = F, # Set to T to induce increase in degree among vaccinated susceptible and vaccinated, infected, undiagnosed individuals
risk_comp_degree_rr = 1.3,
prob_sex_by_age = FALSE,
prob_sex_age_19 = 0.285, # used when prob_sex_by_age == TRUE
max_age_sex = 75, # used when prob_sex_by_age == TRUE
aids_sex_cutoff_prop = 0.47, #proportion of time-in-aids afterwhich coital acts cease; Holllingsworth 2008
mean_sex_acts_day = 0.2,
disclosure_prob = 0.9, # PUMA - seems high and may want to revisit; also should perhaps impact condom use rather than coital freq (as per Mardham)
act_redux_discl = 0.0, # MARDHAM
condom_prob = 0.5,
condom_prob_change = F, # set to true for condom_prob to be 0 initially, and increase as a hill function governed by below parameters
condom_prob_max = 0.5, # parameter used in hill function if condom_prob_change == T
condom_prob_inflect = 365*12, # parameter used in hill function if condom_prob_change == T
condom_prob_pow = 4.1, # parameter used in hill function if condom_prob_change == T
RR_cond_male_concurrent = 1.438,
RR_cond_fem_concurrent = 1.0,
sti_prob = 0.0,
sti_prob_att = NA,
circum_prob = 0.85,
circum_prob_chg = c(0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.85),
circum_prob_yr_chg = NA, # c(12*365, 13*365, 15*365, 16*365, 18*365, 20*365, 22*365, 24*365),
prop_AI = 0.10,
mean_prop_acts_AI = 0.4266,
sd_prop_acts_AI = 0.2146,
role_props = c('I'=0.24, 'R'=0.27, 'V'=0.49), #to activate msm roles, set role props to c("I"=x,"R"=y,"V"=z), where x+y+z=1 and are >=0
role_trans_mat = matrix(c(1,0,0,0,1,0,0,0,1),
nrow=3,dimnames=list(c("I","R","V"))),
prob_iev = 0.4, # Average of Mardham and PUMA
generic_nodal_att_values = NA, # names of generic attributes (eg, 1:5)
generic_nodal_att_values_props = NA, # proportions of each att in initial pop
generic_nodal_att_values_props_births = NA, #how new values distributed with addtns to pop
generic_nodal_att_no_categories = NA, # how many generic att categories
generic_nodal_att_trans_mat = NA) # matrix of per timestep transition probs, each row sums to one
# end of initial list
}
EvoNetHIV/Test_and_Treat documentation built on Feb. 20, 2021, 12:09 a.m.
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Defines functions input_parameters_primary_DEPRECATED
#NOTE!
# This function is now deprecated.
# When called, fxn "input_params" will be called instead
# So, put all new parameters in the "input_params" fxn argument list
#
input_parameters_primary_DEPRECATED <-function(){
#NOTE!
# This function is now deprecated.
# When called, fxn "input_params" will be called instead
# So, put all new parameters in the "input_params" fxn argument list
#
list(
#-- Basic model setup parameters -------------#
model_name = "evomodel",
hpc = FALSE, #on hyak?
output_path = getwd(),
start_timestep = 1, #parameter for EpiModel, should be "1" if new simulation
#if re-starting simulation,
#value should be "n_steps+1" (n_steps from original sim.)
# new parameters for alternative restart routines
restart_val = "none", # "save" = save simulation, "restart" = restart simulation
restart_time = 1e9, # time that base simulation results get saved
restart_tx_type = "CD4_low",
fast_edgelist = FALSE,
nsims = 1,
initial_pop = 100, #initial popn
n_steps = 365*2,
initial_infected = 20,
model_sex = "msm",
scrolling_output = TRUE,
print_frequency = 10, # Set to 10 to print to output screen every 10 days. Default should be 1.
plot_nw = TRUE,
plot_mean_degree_by_age = FALSE, # Set to true to see plots of mean degree broken out by age.
network_print_frequency = 100,
save_network = FALSE,
save_coital_acts = FALSE,
save_vl_list = FALSE, #TRUE to graph individual agent VL
save_infection_matrix=FALSE,
popsumm_frequency=1, #frequency of timesteps should popsumm stats be calculated
estimate_new_nw = TRUE, #for "master_script_loop", if false, only 1 nw estimated
ncores =1, #16 if running on hyak using EpiModelHPC
save_partner_list = FALSE,
#--QA/QC terms ----------------------------#
QA_QC_pause_time = 3, # Time delay in seconds that the computer waits so that users can view QA/QC stats
#-- Network estimation terms -------------#
modes = 1, #epimodel param, will change to 2 in "input_params_derived" if hetero model
nw_form_terms = "~edges + offset(nodematch('role', diff=TRUE, keep=1:2))",
nw_coef_form = c(-Inf, -Inf),
target_stats = 100*0.7/2,
relation_dur = 50,
nw_constraints = " ~.",
dissolution="~offset(edges)",
rm_offset_rel = F, # temporary solution to remove same-role (MSM simulations) and same-sex (heterosexual simulations) relationships. If = T, function remove_offset_relationships will be called in initialize_module.
#-- virulence/vl/transmission model parameters -------------#
VL_Function = "aim2", # Other option is "aim3" (aim 3 code)
vl_peak_agent_flag = FALSE,
transmission_model = "hughes",
max_spvl_allowed = 7.0,
min_spvl_allowed = 2.0,
max_time_inf_initial_pop = 365*2,
vl_full_supp = 13.0, # final viral load after complete treatment
vl_undetectable = 50.0,
vl_exp_decline_tx = -0.6,
InfRateBaseline = 0.0000003, # 0.0001178/365 # Lingappa 2010
InfRateExponent = 3.52, # Lingappa 2010
MaxInfRate = 0.002, # Asymptotic function from Fraser 2007, Assuming P_inf(1 year) = 1 - (1 _ P_inf(1 day))^365
VHalfMaxInfRate = 13938, # Fraser 2007
HillCoeffInfRate = 1.02, # Fraser 2007
shape_parameter = 3.46, # 3.46 is from Fraser
Dmax = 9271, # from Fraser, maximum time in days of asymptomatic state
D50 = 3058, # from Fraser, spVL at which duration is half maximum
Dk = 0.41, # 0.41 is from Fraser, Hill coefficient
V0 = 1e-4,
vl_peak_acute = 7.7e6, # Average viral load during primary infection; from Little et al 1999
vl_max_aids = 2.4e6, # Piatak 1993
vl_increase_AIDS = 1.0041122, #determines slope of vl increase after aids onset; defaault value implies 400-fold increase over 4 years. E.g. O’Brien and Hendrickson 2013.
t_peak = 21.0,
t_acute = 90,
t_acute_phase2 = 32, # Changed on 3/29/16 to give more realistic acute phase dynamics with the revised acute phase code
acute_decline_phase2_rate = -.03,
vl_decay_rate_phase2 = 0.02, #new on 3/2/16, revised acute decline code
AverageLogSP0 = 4.5,
VarianceLogSP0 = 0.8,
MaxPPP = 0.0, # Maximum per pathogen pathogenecity (PPP). Viruses w/ PPP's > 0.0 kill hosts faster for a given SPVL
MutationVariance = 0.01,
max_vl_viralcont_spvl = 7,
min_vl_viralcont_spvl = 2.5,
prog_rate = 0.14, # per year rate. Note VL progression cartoons typically show ~0.5 Log increase in VL over a 8-year
# period. That suggests a progression rate of ln(10^0.5) / 8 = ~0.14 per year
Heritability = 0.36, # desired population heritability estimate is 0.36 (from Fraser; should vary by population). This value, in conjunction with the default mutation variance, seems to yield a stable population heritability of 3.6. But user should always beware!
Flat_Viral_Load = 0, # Setting this to 1 forces VL to be the same value during the entire primary infection period
trans_lambda = 0.000247, # From Steve Goodreau's summary of discussions he had with Jim Hughes
trans_lambda_init = 0.003,
trans_lambda_alpha = 5,
trans_RR_uses_condoms= 0.22, # From Table 4 from Hughes et al. (2012, JID)
trans_RR_LogV = 2.89, # From Table 4 from Hughes et al. (2012, JID)
trans_VLbase = 4.0, # From Steve Goodreau's summary of discussions he had with Jim Hughes
trans_RR_circumcised = 0.53, # From Table 4 from Hughes et al. (2012, JID)
trans_RR_age = 0.67, # Older people have lower risk (Note: 0.82 in the publication. 0.67 comes from Steve after talking to Jim Hughes) [Note that Table 3 from Hughes et al. 2012 gives 0.82]
max_age_RR_age = 45, #threshold age, where ages above threshold have same RR
trans_base_age = 35, # From Steve Goodreau's summary of discussions he had with Jim Hughes
trans_RR_STI = 2.7, # placeholder for generic sti
trans_RR_insertive_anal_msm = 2.9, #From Patel 2014, but adjusted for circumcision. See Evonet params.xls and V-A transmission ratios.xlsx. Was 5 until 7/6/2016
trans_RR_receptive_anal_msm = 17.3, #From Patel 2014, but adjusted for circumcision. See Evonet params.xls and V-A transmission ratios.xlsx. Was 10 until 7/6/2016
trans_RR_ins_ai = 2.9,
trans_RR_rec_ai = 17.3,
trans_RR_acute_phase = 1.0, #increased infectiousness during acute phase
trans_RR_receptive_vaginal=1,
trans_RR_vaccine = 0.01, #placeholder, 5/3/16
perc_virus_vaccine_sens = 0.99, #placeholder 5/3/16
# Parameters for newly added (11/8/15) dynamic CD4 function (revised 11/11/15)
cd4_homo_input = 0.04, # Rate of addition of CD4 T-cells per day when CD4 < 1000
k_cd4 = 0.127, # Rate at which virus kills CD4 T-cells [using log10(virus)]
vl_kill_cd4 = 0.38, # VL below which virus no longer kills off CD4 T-cells
min_prop_blood = 0.5, # Proportion of CD4 T-cells that stay in blood no matter how high VL gets
V_half_redist = 4e5, # Viral load at which 50% of mobilizable CD4 T-cells migrate to lymph nodes
CD4count_end_stage = 1, # CD4 count at which patients die of AIDS
# -- Viral Load / Virulence parameters specific for Aim 3 modeling
# Note with changes made 8/31/15: V_peak, V_SPVL, and V_AIDS are now defined by Aim 2 values given above.
Save_VL_Histories = FALSE,
Max_Allowable_Loci = 5,
prob_CYP_6_slow = 0,
step_size_C = 0.0004, # Step size in C program simulator
s_CD4 = 10, # Rate of input of CD4 T-cells from the thymus
m_CD4 = 0.01, # Natural death rate of CD4 T-cells
k = 0.0000001, # Rate at which viruses kill off CD4 T-cells
r_inf_cells = 1.8, # growth rate of infected cells at start of infection
r_r_inf_cells = 1.3, # growth rate of drug resistant infected cells at start of infection
d_inf_cells = 0.6, # death rate of infected cells
f_M = 0.02, # Fraction of target cells that become moderately long-lived infected cells upon infection
f_L = 1e-6, # Fraction of target cells that become very long-lived (latently) infected cells upon infection
d_M = 0.04, # Death rate of moderately long-lived infected cells
d_L = 0.001, # Death rate of latently infected cells
p_inf_cells = 1000, # Rate at which actively infected cells produce virus
p_M = 100, # Rate at which moderately long-lived infected cells produce virus
p_L = 10, # Rate at which latently infected cells produce virus
M_act = 0.01, # Rate at which M cells convert into productively infected cells, I
L_act = 0.0005, # Rate at which latently infected cells convert into productively infected cells
c = 50, # Clearance rate of free virus (Initial rate prior to virus damaging the immune system)
mu = 3e-5, # Rate at which viruses mutate from being sensitive to being resistant
TransBottleneck = 3e8, # Reduction in viral load associated with transmission
no_loci = 5, # Number of loci conferring drug resistance
cost1 = 0.05, # Fitness cost of having a drug resistance mutation at locus 1
cost2 = 0.05, # Fitness cost of having a drug resistance mutation at locus 2
cost3 = 0.05, # Fitness cost of having a drug resistance mutation at locus 3
cost4 = 0.05, # Fitness cost of having a drug resistance mutation at locus 4
cost5 = 0.05, # Fitness cost of having a drug resistance mutation at locus 5 (note: this mut negates cost at locus 2)
cost_reduct4on2 = 0.9, # Extent to which mutation 4 mitigates the cost of mutation 2
cost_reduct5on1 = 0.5, # Extent to which mutation 5 mitigates the cost of mutation 1
additive_fitness = 0, # 1: fitness = 1 - cost1 - cost2 -... - cost5. <>1: multiplicative: Fitness = (1-cost1)*(1-cost2)*...(1-cost5)
DrugDose1 = 200, DrugDose2 = 200, DrugDose3 = 200.0, DrugDose4 = 200.0, # Dose of drugs taken
drug_decay1 = 1.0, # Per day clearance rate of drug 1
drug_decay2 = 1.0, # Per day clearance rate of drug 2
drug_decay3 = 1.0, # Per day clearance rate of drug 3
drug_decay4 = 1.0, # Per day clearance rate of drug 3
drug_2nd_decay1 = 0.1, # Second-phase decay rate drug 1
drug_2nd_decay2 = 0.1, # Second-phase decay rate drug 2
drug_2nd_decay3 = 0.1, # Second-phase decay rate drug 3
drug_2nd_decay4 = 0.1, # Second-phase decay rate drug 4
conc_2nd_phase1 = 1.0e-70, # Drug concentration at which second-phase starts for drug 1
conc_2nd_phase2 = 1.0e-70, # Drug concentration at which second-phase starts for drug 2
conc_2nd_phase3 = 1.0e-70, # Drug concentration at which second-phase starts for drug 3
conc_2nd_phase4 = 1.0e-70, # Drug concentration at which second-phase starts for drug 4
min_adherence1 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence1 = 1,
min_adherence2 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence2 = 1,
min_adherence3 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence3 = 1,
min_adherence4 = 0, # Each person has an randomly choosen adherence level ranging from min_adherence to max_adherence.
max_adherence4 = 1,
adherence_type = 1:2, #1=random, 2=cyclic
adherence_type_prob=c(1,0),#default: all agents adherence type 1,
adherence_days_high=5,#for cyclic adherence
adherence_days_low=2,#for cyclic adherence
aherence_days_high_prob=0.9,#for cyclic adherence
aherence_days_low_prob=0.1,#for cyclic adherence
BaseIC50Drug1 = 200.0, BaseIC50Drug2 = 200.0, BaseIC50Drug3 = 200.0, # Concentration of drug that blocks V by 50%
BaseIC50Drug4 = 2.0, # Drug 4 is some super-effective 2nd-line therapy combo
Interaction_Model_Drugs12 = 1, # 1 = Bliss independence, 2 = Simple saturation (Huang et al. 2003),
# 3 = Lowe additivity (not yet implemented)
# Background: Current parameterizations assumes that drugs 1 and 2 are both NRTIs
# Therefore, they may compete for the active site, reducing the degree of inhibition
# Interaction_Model_Drugs12 implements different math ideas for how they may combine
FC_D1_Mut1 = 50.0, # Effect of mutation 1 on the IC50 value of drug 1. (Fold-change from baseline)
FC_D1_Mut2 = 1.0,
FC_D1_Mut3 = 1.0,
FC_D1_Mut4 = 1.0,
FC_D1_Mut5 = 1.0,
FC_D2_Mut1 = 1.0,
FC_D2_Mut2 = 50.0,
FC_D2_Mut3 = 1.0,
FC_D2_Mut4 = 1.0,
FC_D2_Mut5 = 1.0,
FC_D3_Mut1 = 1.0,
FC_D3_Mut2 = 1.0,
FC_D3_Mut3 = 10.0,
FC_D3_Mut4 = 5.0,
FC_D3_Mut5 = 1.0,
FC_D4_Mut1 = 1.0,
FC_D4_Mut2 = 1.0,
FC_D4_Mut3 = 1.0,
FC_D4_Mut4 = 1.0,
FC_D4_Mut5 = 1.0,
StochasticCut = 1.0e-6, # Density of cells (or viruses) below which cell changes occur stochastically
AbsoluteCut = 1.0e-7, # Density of cells (or viruses) that correspond to 1 cell per body.
Dosing_Interval =1,#Dosing_Interval =1 means once daily dosing,Dosing_Interval = 2 means twice daily dosing
Therapy_Type = 1,#Therapy_Type = 1 eans three individual pills Therapy_Type = 2 means that drugs 1 and 2 are contained within a single pill, Therapy_Type = 3 means that all three drugs are contained within a single pill
# Stockout parameters (e.g., no gets Drug 1 btw StopDrug1 and RestartDrug1)
# These apply to all patients regardless of their adherence
StopDrug1 = 1000000000.0, # Stock out time (in days) for drug 1
RestartDrug1 = 1000000000.0, # Drug 1 becomes available again
StopDrug2 = 1000000000.0, # Stock out time (in days) for drug 2
RestartDrug2 = 1000000000.0, # Drug 2 becomes available once again
StopDrug3 = 1000000000.0, # Stock out (in days) for drug 3
RestartDrug3 = 1000000000.0, # Drug 3 becomes available once again
StopDrug4 = 1000000000.0, # Stock out (in days) for drug 3
RestartDrug4 = 1000000000.0, # Drug 3 becomes available once again
# -- Parameters related to viral load testing
prob_elig_vl_test = 0.75,
time_on_tx_for_vl_testing = 365/2,
vl_testing_interval = 365/2,
# -- Parameters related to PrEP / Vaccine models that use alleles to model resistance
PrEP_Model = FALSE,
# -- Parameters related to drug resistance testing and start of 2nd line therapy (aim 3 only)
resist_testing_model = "interval",
mean_resist_test_interval = 182,
time_on_tx_for_resist_testing = 182, # Days on tx before clinicians test for resistance
VL_thres_resist_testing = 1e3, # Viral load at which clinicians will do a resistance test
no_muts_switch_2nd_line = 1, # Num resist mutations needed to trigger switch to 2nd-line therapy
#-- vital dyamics model parameters -------------#
min_age = 18,
max_age = 55,
age_dist_new_adds = "min_age", # "mixed" (some min_age, others older)
#or "linear_decline_18_55"
prop_new_agents_min_age = 0.45, #for "mixed" see above line
asmr_data_male = "usa_men_18_to_100",#other option: "south_africa_male"
asmr_data_female = "south_africa_female",
initial_agedata_male = "usa_men_18_to_100", #other options:"south_africa_male_16_to_100", "stable_age_no_hiv_dist"
initial_agedata_female = "south_africa_female_16_to_100_2014", #other options: "stable_age_no_hiv_dist"
aids_death_model = "cd4", # c("Gamma_Death","daily_prob","cd4")
death_rate_constant = 0.000003, # 0.000003 is from CASCADE
death_rate_exponent = 6.45, # 6.45 is from CASCADE
cd4_cat1_death_prob = 0.0000112, #prob. of death for cd4 cat1
cd4_cat2_death_prob = 0.0000148, #prob of death for cd4 cat2
cd4_cat3_death_prob = 0.0000333, #prob of death for cd4 cat3
cd4_cat4_treated_death_prob = 0.0000760, #prob death for cd4 cat4(aids) on tx
cd4_prob_incr_nadir = 0.03, #prob of improving one cd4 cat from nadir
cd4_prob_incr_nadir_minus= 0.0005, #prob of improving one cd4 cat from nadir -1
time_in_aids = 475,
birth_model = "poisson_birth_numbers", # "births=deaths", "poisson_birth_numbers", "exponential_growth","constant_rate", "constant_number", "exponential_growth"
baseline_input_exp_growth = 0.007, # Used with "exponential_growth" MUST BE SCALED BY HAND TO GET A STABLE AGE DISTRIBUTIONbirth_model = "poisson_birth_numbers", # "births=deaths", "poisson_birth_numbers", "constant_rate", "constant_number"
contstant_birth_number = 0,
constant_birth_rate = 0.0001306,
poisson_birth_lambda_base = 0.01370, #scaled to init pop size in 'input_derived_parameters', 7/720/16
pop_growth_rate_annual = 0.01, # as proportion, x100 for percent
constant_rate_spread_out = .01, #birth model: "constant_rate_spread_out"
births_per_year = 1, #birth model: "constant_number_spread_out"
#-- social / treatment /testing parameters -------------#
testing_model = "interval",
no_past_partners_time_prep = 365,
min_past_partners_prep = 1,
min_current_partners_prep = 1,
min_pos_partner_duration = 50,
prep_recent_test = 183,
percent_eligible_on_prep = 1,
start_prep_campaign = 5e5,
prob_tx_droput = 0,
percent_condom_users= 1, # non-users never use condoms when partnered with other non-users. users will use condoms with some probability
compact_el_divisor=1e5,
ave_rel_dur_start = 5*365,
condom_use_rel_dur = FALSE,
condom_use_age = FALSE,
age_condom_use_halves = 50, # Only used when condom_use_age is true
mean_test_interval_male = 365,
mean_test_interval_female = 442,
mean_test_interval_under25 = 365,
test_result_delay = 35, # Delay between being infected and having sufficient antibodies for diagnosis
reduction_test_interval_enhanced = 0.1, # fage reduction in test interval for those identified for "enhanced" testing
prob_enhanced_testing_before_campaign = 0.3, # Percentage of population that gets tested more frequently after scale-up campaign
prob_enhanced_testing_after_campaign = 0.7, # Percentage of population that gets tested more frequently after scale-up campaign
testing_limit = "percent_agents", # For models in which there is an upper limit to number tested. Other is "percent_agents_minus_diagnosed"
tx_type = NA,
mean_trtmnt_delay = 1,
mean_time_tx = 0, # used when tx_type = "time_dist"
sd_time_tx = 0, # used when tx_type = "time_dist"
start_treatment_campaign = 5e5,
tx_limit = "absolute_num", # used with social_treatment_module_multiple_criteria.
max_num_treated = 5e5,
max_num_treated_begin = 5e5, # Num during pre-campaign, used with social_treatment_module_multiple_criteria
max_num_treated = 5e5, # Num during big campaign, used with social_treatment_module_multiple_criteria
proportion_treated = 1,
yearly_incr_tx = 0, # Setting this to 0.1 would mean 10% more people get treated each year
proportion_treated_begin = 0.0,
start_treat_before_big_campaign = 5e5,
num_randomly_chosen_start_campaign = 0, # Number of people outside the prioritized group who get treated (this is actually an outcome rather than a parameter)
num_treated_start_campaign = 0, # Total number of people treated at the start of the campaign (this is actually an outcome rather than a parameter)
num_enhanced_start_campaign = 0, # number diagnosed or receiving enhanced testing (this is actually a variable)
# Below four parameters are used in social_treatment_sex_age module
cov_prob <- c(0.0, 0.01, 0.021, 0.030, 0.049, 0.100, 0.191, 0.283, 0.402, 0.78), # Coverage levels corresponding to years below
cov_prob_yrs <- c(0, 11:18, 23), # Years at which coverage level changes
cov_prob_scal <- matrix(c(0.7242, 0.9955, 1.2593, 0.5895, 0.8103, 1.0251), ncol = 2, dimnames = list(rep("", 3), c("f", "m"))), # values are by sex, then by age within sex
cov_prob_ageg <- list(c(15, 25), c(25, 35), c(35, 56)),
# -- additional treatment parameters --- #
under_25_flag = FALSE,
tx_in_acute_phase = FALSE,
tx_schedule_props =c("F"=1,"V"=0,"N"=0,"P"=0),
treatment_threshold = 1e4, #VL raw,not log10 transformed
cd4_treatment_threshold = 0,
cd4_trt_guidelines_chgs = NA, # Default for SA eligibility changes: list(4, 3:4, 2:4, 1:4)
attr_treatment_threshold = 5, # Under development. When finished, this parameter will
# allow one to target therapy to attribute groups
# "attr_treatment_threshold" and higher (e.g. target therapy
# to people with the most connnections)
min_inf_time_for_treat = 0, # number of timesteps (days)
treat_thresh_partners = 0,
max_treated = NA, #relevant for "social_treatment_john" module,
#this value is set within module at start of tx campaign,
#NA value is flat that indicates it hasn't been initialized yet
prob_care = 1.0, # Probability of being eligible for care before the "under care" campaign
prob_eligible_ART = 1.0,
prob_eligible_2nd_line_ART = 1.0,
second_line_elig = "vl_only",
# -- targeted efforts to get more people under care after "scale_up" compaign --------#
start_scale_up_campaign = 5e5, # At this time, increase the number the number of people eligible for care
prob_care_before_campaign = 1.0, # Probability of being eligible for care after the "under care" campaign (should be >= prob_care)
prob_care_after_campaign = 1.0, # Probability of being eligible for care after the "under care" campaign (should be >= prob_care)
scale_up_type = "random", # Method for prioritizing people for "under care" campaign. See the new routine
# "social_bring_new_groups_into_care" for list of other options (e.g., "AIDS", "under30")
min_age_recruit_for_care = 0.0, # Parameter that allows targeting of people of intermediate ages to get under care
max_age_recruit_for_care = 100, # Parameter that allows targeting of people of intermediate ages to get under care
# -- vaccine parameters --------#
start_vacc_campaign = 5e5,
perc_vaccinated = 0.5,
target_vacc_att = FALSE,
vacc_eff_duration = 365*3,
risk_comp_cond = F, # Set to T to induce reduction in condom use among vaccinated susceptibles and vaccinated, infected, undiagnosed individuals.
risk_comp_cond_rr = 0.70,
risk_comp_degree = F, # Set to T to induce increase in degree among vaccinated susceptible and vaccinated, infected, undiagnosed individuals
risk_comp_degree_rr = 1.3,
prob_sex_by_age = FALSE,
prob_sex_age_19 = 0.285, # used when prob_sex_by_age == TRUE
max_age_sex = 75, # used when prob_sex_by_age == TRUE
aids_sex_cutoff_prop = 0.47, #proportion of time-in-aids afterwhich coital acts cease; Holllingsworth 2008
mean_sex_acts_day = 0.2,
disclosure_prob = 0.9, # PUMA - seems high and may want to revisit; also should perhaps impact condom use rather than coital freq (as per Mardham)
act_redux_discl = 0.0, # MARDHAM
condom_prob = 0.5,
condom_prob_change = F, # set to true for condom_prob to be 0 initially, and increase as a hill function governed by below parameters
condom_prob_max = 0.5, # parameter used in hill function if condom_prob_change == T
condom_prob_inflect = 365*12, # parameter used in hill function if condom_prob_change == T
condom_prob_pow = 4.1, # parameter used in hill function if condom_prob_change == T
RR_cond_male_concurrent = 1.438,
RR_cond_fem_concurrent = 1.0,
sti_prob = 0.0,
sti_prob_att = NA,
circum_prob = 0.85,
circum_prob_chg = c(0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.85),
circum_prob_yr_chg = NA, # c(12*365, 13*365, 15*365, 16*365, 18*365, 20*365, 22*365, 24*365),
prop_AI = 0.10,
mean_prop_acts_AI = 0.4266,
sd_prop_acts_AI = 0.2146,
role_props = c('I'=0.24, 'R'=0.27, 'V'=0.49), #to activate msm roles, set role props to c("I"=x,"R"=y,"V"=z), where x+y+z=1 and are >=0
role_trans_mat = matrix(c(1,0,0,0,1,0,0,0,1),
nrow=3,dimnames=list(c("I","R","V"))),
prob_iev = 0.4, # Average of Mardham and PUMA
generic_nodal_att_values = NA, # names of generic attributes (eg, 1:5)
generic_nodal_att_values_props = NA, # proportions of each att in initial pop
generic_nodal_att_values_props_births = NA, #how new values distributed with addtns to pop
generic_nodal_att_no_categories = NA, # how many generic att categories
generic_nodal_att_trans_mat = NA) # matrix of per timestep transition probs, each row sums to one
# end of initial list
}
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