R/input_parameters_primary.R

Defines functions input_parameters_primary

#' @export
input_parameters_primary<-function(){

  # Description:
  # List of most parameter values needed to run evonet.
  # Parameter commonly changed in master_script.R include:
  #   AverageLogSP0, VarianceLogSP0, condom_prob, sti_prob, circum_prob, start_treatment_campaign,
  #   and birth_model

   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.)
    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,
    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
  #-- Network estimation terms -------------#

    modes         = 1,
    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 = " ~.",

#-- 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,
    tx_type               = NA,
    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_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_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,

    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)
    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
    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
    DrugDose1 =  200, DrugDose2 = 200, DrugDose3 = 200.0,  DrugDose4 = 200.0, # Dose of drugs taken
    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
    testing_model            = "interval",
    mean_test_interval_male  = 365,
    mean_test_interval_female = 442,

# -- 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",
    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", "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 -------------#

    start_treatment_campaign = 5e5,
    start_vacc_campaign      = 5e5,
    prob_care                = 1.0,
    prob_eligible_ART        = 1.0,
    prob_eligible_2nd_line_ART = 1.0,
    perc_vaccinated          = 0.5,
    vacc_eff_duration        =  365*3,
    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,
    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
    proportion_treated       = 1,
    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,
    RR_cond_male_concurrent  = 1.438,
    RR_cond_fem_concurrent   = 1.0,
    sti_prob                 = 0.0,
    sti_prob_att             = NA,
    circum_prob              = 0.85,
    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/EvoNetVaccine documentation built on June 2, 2017, 10:27 a.m.