R/Initial_conditions.R

In mattg3004/SchistoIndividual: Individual based simulations of schistosomaisis.

N = 10000    #population size
max_age = 100
initial_worms = 0
time_step = 10
worm_stages = as.integer(1)
female_factor = 1
male_factor = 1


# number of eggs which is classified as heavy burden
heavy_burden_threshold = 16
#= this is the number of thousands of people in 5 year (0-4, 5-9,...) intervals in Kenya
#and will be used to give a specified age structure when we run to equilibrium =#
spec_ages = c(8639, 9082, 6424, 5074, 4425, 3847, 3628, 3062,
              2436, 1770, 1868, 1066, 743, 518, 355, 144)

# specify the number of ages in each bracket in the above specified ages (e.g. 7639 in the 0-4 age bracket and 7082 in the 5-9 age bracket)
ages_per_index = 5   

# if more than one ommunity, then specify how many here
#N_communities = 3
N_communities = 1

# next parameter is the relative probabilities of being in each community
# if entries are all equal, then all communities are equally likely and will
# be roughly the same size
#community_probs = c(1,1,1)
community_probs = 1
community_probs = JuliaCall::julia_eval("[1.0]", need_return =  "Julia")


# community contact rates give the contact rate with the infectious
# cercariae pool for each community
community_contact_rate = c(1,0.5,0.25)
community_contact_rate = 1
community_contact_rate = JuliaCall::julia_eval("[1.0]", need_return =  "Julia")

# parameter for proportion of people who are given mda who will take it
mda_adherence = .9
mda_access = .9


# how long to run simulation for
number_years = 250

max_fecundity = 0.34  # for S. mansoni [Toor et al JID paper SI]
#max_fecundity = 0.3  # for S. haematobium [Toor et al JID paper SI]

density_dependent_fecundity = 0.0007 # for S. mansoni [Toor et al JID paper SI]
#density_dependent_fecundity = 0.0006 # for S. haematobium [Toor et al JID paper SI]

# number of days after which miracidia become cercariae
miracidia_maturity_time = 24 # for S. mansoni 
# miracidia_maturity_time = 21 # for S. haemotobium 

initial_miracidia = 100000*N/1000
init_env_cercariae = 100000*N/1000


initial_miracidia_days = round(miracidia_maturity_time/time_step)


r = 0.03 # aggregation parameter for negative binomial for egg production
num_time_steps = as.integer(365*number_years / time_step)

# human birth rate
birth_rate = 28*time_step/(1000*365)

average_worm_lifespan = 5.7 # years for S. mansoni [Toor et al JID paper SI]
#average_worm_lifespan = 4 # years for S. haematobium [Toor et al JID paper SI]

# this is the aggregation parameter for the predisposition
predis_aggregation = 0.24 # 0.24 for high prev settings; 0.04 for low prev settings # From "The design of schistosomiasis monitoring and evaluation programmes:
#The importance of collecting adult data to inform treatment strategies for Schistosoma mansoni"
predis_weight = 1


mda_coverage = 0.8 # proportion of target age group reached by mda
mda_round = 0

# proportion of cercariae which can infect humans
human_cercariae_prop = 1

# gamma distribution for Kato-Katz method
kato_katz_par = 0.87
use_kato_katz = 0
# gamma_k = Gamma(0.87,1/0.87)
vaccine_effectiveness = 0.95

# record the state of the population this often in years
record_frequency = 1/24 # use 1/12 for monthly output
drug_effectiveness = 0.863

#= number of deaths per 1000 individuals by age
#first entry is for under 1's, then for 5 year intervals from then on =#
# age_death_rate_per_1000 = c(6.56, 0.93, 0.3, 0.23, 0.27, 0.38, 0.44, 0.48,0.53, 0.65,
#                             0.88, 1.06, 1.44, 2.1, 3.33, 5.29, 8.51, 13.66,
#                             21.83, 29.98, 36.98)

death_prob_by_age = c(0.0656, 0.0093, 0.003, 0.0023, 0.0027, 0.0038, 0.0044, 0.0048, 0.0053,
                      0.0065, 0.0088, 0.0106, 0.0144, 0.021, 0.0333, 0.0529, 0.0851, 0.1366, 0.2183, 0.2998 , 0.3698, 1)

ages_for_death = c(1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60,
                   65, 70, 75, 80, 85, 90, 95, 100, 110)


filename = "population.jld"
N = 1000
N = as.integer(N)
worm_stages = as.integer(worm_stages)
scenario = "moderate adult"


input_ages = array(0,dim=c(0))
input_contact_rates = array(0,dim=c(0))
max_age = 100
max_age = as.integer(max_age)

number_years_equ = 300 #for low prev settings, may need more years to check stable

num_time_steps_equ = as.integer(365*number_years_equ / time_step)
contact_rate = 0.09
human_cercariae_prop = 1
cercariae_survival = 1/2
miracidia_survival = 1/2
M0 = 15

col1 = rgb(110/255, 99/255, 252/255)
col1a = rgb(110/255, 99/255, 252/255,0.3)
col2 = rgb(30/255, 190/255, 160/255)

col2a = rgb(30/255, 190/255, 160/255,0.3)
col3 = rgb(2/255, 163/255, 217/255)
col3a = rgb(2/255, 163/255, 217/255,0.3)


rate_acquired_immunity = 0

pars = set_pars(N,
                time_step,
                N_communities,
                community_probs,
                community_contact_rate,
                density_dependent_fecundity,
                average_worm_lifespan,
                max_age,
                initial_worms,
                initial_miracidia,
                initial_miracidia_days,
                init_env_cercariae,
                worm_stages,
                contact_rate,
                max_fecundity,
                age_contact_rates,
                ages_for_contacts,
                contact_rate_by_age_array,
                mda_adherence,
                mda_access,
                female_factor,
                male_factor,
                miracidia_maturity_time,
                birth_rate,
                human_cercariae_prop,
                predis_aggregation,
                cercariae_survival,
                miracidia_survival,
                death_prob_by_age,
                ages_for_death,
                r,
                vaccine_effectiveness,
                drug_effectiveness,
                spec_ages,
                ages_per_index,
                record_frequency,
                use_kato_katz,
                kato_katz_par,
                heavy_burden_threshold,
                rate_acquired_immunity,
                M0,
                input_ages, 
                input_contact_rates)