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defaults
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defaults
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##########################################################
#
# SIMULATION SETUP PARAMETERS
#
##########################################################
# Length of each run
days = 240
# Simulation start date in the format 'YYYY-MM-DD'
start_date = 2012-01-02
# Whether to rotate through 7 start dates on multiple runs
rotate_start_date = 0
# Seed for RNG
seed = 123456
# Day to reset seed
reseed_day = -1
##### Geographical grids
use_mean_latitude = 1
# Size of large-scale grid patches in km
regional_patch_size = 20.0
# size of neighborhood patches in km
neighborhood_patch_size = 1.0
#### Probabilistic sensitivity analysis
psa_sample_size = 0
psa_sample = 0
psa_method = LHS
# psa_method = RAND
psa_list_file = $FRED_HOME/input_files/PSA/psa_params.txt
##########################################################
#
# OUTPUT CONTROL PARAMETERS
#
##########################################################
verbose = 1
debug = 1
test = 0
outdir = OUT
tracefile = none
track_infection_events = 0
track_age_distribution = 0
track_household_distribution = 0
track_network_stats = 0
# report_age_of_infection
# 1 (Infants, Toddlers, Preschool, Students, Elementary, Highschool, Young_adults, Adults, Elderly)
# 2 = (Infants, Toddlers, Pre-k, Elementary, Highschool, Young_adults, Adults, Elderly)
# 3 = (0_4, 5_17, 18_49, 50_64, 65_up)
# 4 = (Every age from 0 to MAX_AGE)
# 5+ Report by age_of_infection_log_level
report_age_of_infection = 0
report_transmission_by_age = 0
# log_level 0 (min) - 4 (max)
age_of_infection_log_level = 0
report_place_of_infection = 0
report_distance_of_infection = 0
report_presenteeism = 0
report_childhood_presenteeism = 0
report_generation_time = 0
report_serial_interval = 0
report_incidence_by_county = 0
report_incidence_by_census_tract = 0
report_symptomatic_incidence_by_census_tract = 0
report_county_demographic_information = 0
quality_control = 1
print_household_locations = 0
rr_delay = 10
# set non-zero to get headers printed in the trace file
trace_headers = 0
# Parameters to allow for output of Population at scheduled times
# Only done if output_population != 0
output_population = 0
pop_outfile = pop_out
# date match should be in format MM-DD-YYYY with * as a wildcard for any of the fields
output_population_date_match = 01-01-*
# if set, out each person health status changes to logfile
# with tag "HEALTH CHART:"
enable_health_charts = 0
##########################################################
#
# VISUALIZATION DATA COLLECTION (OPTIONAL)
#
##########################################################
enable_visualization_layer = 0
# maximum rows or columns in visualization grid:
visualization_grid_size = 250
gaia_visualization_mode = 0
household_visualization_mode = 0
census_tract_visualization_mode = 0
##########################################################
#
# POPULATION PARAMETERS
#
##########################################################
# location of the synthetic population files
synthetic_population_directory = $FRED_HOME/populations
# locations of default state, county and msa files:
states_file = $FRED_HOME/input_files/countries/usa/US_states.txt
msa_file = $FRED_HOME/input_files/countries/usa/US_msa.txt
counties_file = $FRED_HOME/input_files/countries/usa/US_counties.txt
# The synthetic_population_version will be prepended
# to the FIPS code to form the synthetic_population_id
# RTI 2010 Ver 1:
synthetic_population_version = 2010_ver1
synthetic_population_id = 2010_ver1_42003
# RTI 2005-2009 Ver 2:
# synthetic_population_id = 2005_2009_ver2_42003
# synthetic_population_version = 2005_2009_ver2
num_demes = 1
# Metropolitan or Micropolitan Statistical Area MSA code
# MSA has highest priority
# Used to populate deme
# Overrides synthetic_population_id parameter, FIPS, city, county, and state
msa = none
#
# If any of the following parameters are specified, they will override
# the the synthetic_population_id parameter.
#
# Precedence is in the following order:
# FIPS code,
# city name,
# county name,
# state name
#
# FIPS code overrides a city name, which overrides
# county and so on. In all cases, the name is ultimately
# transformed into a FIPS code.
# For Allegheny County, PA:
# fips = 42003
fips = none
# The city format is "name state_abbreviation", such as:
# city = Pittsburgh PA
city = none
# The county format is "name state_abbreviation", such as:
# county = Allegheny County PA
county = none
# The state format can be the state name or abbreviation, such as:
# state = New York
# state = NY
state = none
# if set, make a private copy of files before reading.
# this is meant to reduce file contention when running
# multiple instances of FRED.
enable_copy_files = 0
##########################################################
#
# EPIDEMIC INITIALIZATION PARAMETERS
#
##########################################################
# schedule of initial seed cases
primary_cases_file = $FRED_HOME/input_files/primary_cases_10_on_day_0.txt
# how are initial infections selected?
seed_by_age = 0
seed_age_lower_bound = 0
seed_age_upper_bound = 120
# control how far into their infection trajectory the seeds are
# "exposed" => all seeded infections start on day 0 [ DEFAULT ]
# "infectious" => all seeded infections start on first infectious day
# "random" => randomly select the day in the infection trajectory with uniform probability
# "exposed:<float>;infectious:<float>" => user-specified fraction of initially
# exposed/infectious seeds (e.g. "exposed:0.25;infectious:0.75"); must sum to ~1.0
advanced_seeding = exposed
# To delay the start of the epidemic up to 6 days. If the value is > 6,
# the system will assign a random delay based on the run number
epidemic_offset = 0
vaccine_offset = 0
# test new transmission model if set
enable_new_transmission_model = 0
enable_transmission_network = 0
# sexual partner network params
enable_sexual_partner_network = 0
sexual_partner_contacts = 0.1
sexual_trans_per_contact = 0.1
##########################################################
#
# PLACE PARAMETERS
#
##########################################################
# Neighborhood Activities
community_distance = 20
community_prob = 0.1
home_neighborhood_prob = 0.5
#### Neighborhood transmission gravity model:
enable_neighborhood_gravity_model = 1
# maximum values for how much data to store for each patch:
neighborhood_max_distance = 25
neighborhood_max_destinations = 100
# gravity model parameters:
neighborhood_min_distance = 4.0
neighborhood_distance_exponent = 3.0
neighborhood_population_exponent = 1.0
# If set, then all workers who have a workplace outside the location file
# are assigned a random workplace in the location file.
enable_local_workplace_assignment = 0
#Household Reporting
report_mean_household_stats_per_income_category = 0
cat_I_max_income = 10000
cat_II_max_income = 15001
cat_III_max_income = 25001
cat_IV_max_income = 35001
cat_V_max_income = 50001
cat_VI_max_income = 100001
report_epidemic_data_by_census_tract = 0
##########################################################
#
# SCHOOL SETUP PARAMETERS
#
##########################################################
# max size per classroom
school_classroom_size = 40
# set to 1 if schools closed during summer
school_summer_schedule = 0
# summer closure dates (format MM-DD)
school_summer_start = 06-01
school_summer_end = 08-31
# if set, then each school within the region is assigned teachers
# by recruiting workers from a nearby workplace
assign_teachers = 1
school_fixed_staff = 5
# from: http://www.statemaster.com/graph/edu_ele_sec_pup_rat-elementary-secondary-pupil-teacher-ratio
school_student_teacher_ratio = 15.5
# reporting parameters
report_mean_household_income_per_school = 0
report_mean_household_size_per_school = 0
report_mean_household_distance_from_school = 0
##########################################################
#
# WORKPLACE SETUP PARAMETERS
#
##########################################################
office_size = 50
# The workplace size max is used to group workplaces into categories based on the max number of employees.
# There will always be one additional grouping from the max listed to MAX_INT
workplace_size_max = 0
##########################################################
#
# SUPPORT FOR HOSPITALS (OPTIONAL)
#
##########################################################
enable_hospitals = 0
# These are the probabilities for general hospital or outpatient healthcare
# see also disease specific values for probabilities
hospitalization_prob_age_groups = 0
hospitalization_prob_values = 0
outpatient_healthcare_prob_age_groups = 0
outpatient_healthcare_prob_values = 0
prob_of_visiting_hospitalized_housemate = 0.0
hospital_fixed_staff = 20
hospital_worker_to_bed_ratio = 1.0
hospital_outpatients_per_day_per_employee = 3.0
healthcare_clinic_outpatients_per_day_per_employee = 12.0;
hospitalization_radius = 25.0
#If there aren’t this many beds, then the Hospital is subtype Clinic (no overnights)
hospital_min_bed_threshold = 10
household_hospital_map_file_directory = $FRED_HOME/input_files/countries/usa/
household_hospital_map_file = none
enable_health_insurance = 0
#### HEALTH INSURANCE MARKET SEGMENTS
#
# 0 = PRIVATE
# 1 = MEDICARE
# 2 = MEDICAID
# 3 = HIGHMARK
# 4 = UPMC
# 5 = UNINSURED
#
# health_insurance_distribution should add up to 100
health_insurance_distribution = 6 0 0 0 0 0 100
# hospital accepts insurance probabilities
hospital_health_insurance_prob = 6 0.0 0.0 0.0 0.0 0.0 1.0
##########################################################
#
# SUPPORT FOR GROUP QUARTERS
#
##########################################################
enable_group_quarters = 1
college_dorm_mean_size = 2.5
college_fixed_staff = 2
college_resident_to_staff_ratio = 5.0
military_barracks_mean_size = 8.0
military_fixed_staff = 5
military_resident_to_staff_ratio = 10.0
prison_cell_mean_size = 1.5
prison_fixed_staff = 5
prison_resident_to_staff_ratio = 10.0
nursing_home_room_mean_size = 1.5
nursing_home_fixed_staff = 5
nursing_home_resident_to_staff_ratio = 10.0
##########################################################
#
# TRAVEL PARAMETERS (OPTIONAL)
#
##########################################################
# Long-distance Overnight Travel:
enable_travel = 0
# cdf of trip duration in days
travel_duration = 9 0 0.2 0.4 0.6 0.67 0.74 0.81 0.9 1.0
# for travel age map
travel_age_prob_age_groups = 9 16 25 35 45 55 65 75 85 120
travel_age_prob_values = 9 0.05 0.12 0.10 0.30 0.17 0.14 0.07 0.05 0.00
# distance threshold for overnight trips (in km)
min_travel_distance = 100.0
# list of travel hubs
travel_hub_file = $FRED_HOME/input_files/countries/usa/msa_hubs.txt
# matrix of trips per day between hubs
trips_per_day_file = $FRED_HOME/input_files/countries/usa/trips_per_day.txt
##########################################################
#
# SCHOOL CLOSURE POLICIES
#
##########################################################
school_closure_policy = none
# school_closure_policy = global
# school_closure_policy = individual
# number of days to keep a school closed
school_closure_duration = 10
# if Weeks not -1, set school_closure_duration = 7 * Weeks
Weeks = -1
# delay after reaching any trigger before closing schools
school_closure_delay = 1
# day to close school under global policy
school_closure_day = 10
# global closure triggered when global attack rate >= threshold (if school_closure_day == -1);
school_closure_ar_threshold = 1.0
# day to start checking under individual school closure policy
min_school_closure_day = 1
# individual school closure triggered when the school's attack rate >= threshold
individual_school_closure_ar_threshold = 5.0
# number of cases within a given school that triggers individual school closure
# If -1, use the school_closure_ar_threshold fraction
school_closure_cases = -1
# an alias for school_closure_cases
Cases = -1
##########################################################
#
# ANTIVIRALS (OPTIONAL)
#
##########################################################
enable_antivirals = 0
number_antivirals = 0
## Sample Antiviral 1:
av_disease[0] = 0
av_initial_stock[0] = 100
av_total_avail[0] = 1000
av_additional_per_day[0] = 100
av_course_length[0] = 10
av_reduce_infectivity[0] = .70
av_reduce_susceptibility[0] = 0.30
av_reduce_symptomatic_period[0] = 0.7
av_reduce_asymptomatic_period[0] = 0.0
av_start_day[0] = 0
av_prophylaxis[0] = 0
av_prob_symptoms[0] = 0.677
av_percent_symptomatics[0] = 0.50
av_course_start_day[0] = 1 1.00000
##########################################################
#
# VACCINATION (OPTIONAL)
#
##########################################################
enable_vaccination = 0
number_of_vaccines = 0
#vaccine_tracefile = vacctr
vaccine_tracefile = none
vaccine_prioritize_acip = 0
vaccine_prioritize_by_age = 0
vaccine_priority_age_low = 0
vaccine_priority_age_high = 100
vaccine_dose_priority = 0
vaccine_priority_only = 0
vaccinate_symptomatics = 0
refresh_vaccine_queues_daily = 0
## Sample Definition of Vaccine #0
vaccine_number_of_doses[0] = 1
vaccine_total_avail[0] = 1000000000
vaccine_additional_per_day[0] = 1000000
vaccine_starting_day[0] = 0
vaccine_efficacy_duration_age_groups[0] = 1 120
vaccine_efficacy_duration_values[0] = 1 99999
#### Sample Vaccine #0 Dose #0
vaccine_next_dosage_day[0][0] = 0
vaccine_dose_efficacy_age_groups[0][0] = 1 100
vaccine_dose_efficacy_values[0][0] = 1 0.70
vaccine_dose_efficacy_delay_age_groups[0][0] = 1 100
vaccine_dose_efficacy_delay_values[0][0] = 1 14
vaccine_strains[0] = 0
##########################################################
#
# FLU DAYS INTERVENTION
#
##########################################################
## additional paid sick days in flu season
flu_days = 0
##########################################################
#
# ISOLATION OF SYMPTOMATICS
#
##########################################################
enable_isolation = 0
# days after becoming symptomatic when isolation may occur:
isolation_delay = 1
# daily probability of entering isolation if symptomatic:
isolation_rate = 1.0
##########################################################
#
# HAZard-area primary carE Locator
#
# Note: HAZEL is concerned with Hospitals, Health
# Insurance, Chronic conditions, and visualization of
# such. Also it is not so much about disease transmission.
# So, at least the following should be set
# (along with any specific params needed:
#
# # NO DISEASE TRANSMISSION
# influenza_transmissibility = 0.0
# # HOSPITALS
# enable_hospitals = 1
# # HEALTH INSURANCE
# enable_health_insurance = 1
# # CHRONIC CONDITIONS
# enable_chronic_condition = 1
# # VISUALIZATION
# enable_visualization_layer = 1
# household_visualization_mode = 1
# report_incidence_by_county = 1
#
##########################################################
enable_HAZEL = 0
#Used in Hospital.cc to find file with additional info
# on Hospitals that is not in the standard input file
HAZEL_hospital_init_file_directory = $FRED_HOME/input_files
HAZEL_hospital_init_file_name = none
#Used in Hospital.cc to modulate hospitals reopening
# (if no hospital info file present)
HAZEL_reopening_CDF = 0
#Used in Hospital.cc to increase capacity of Hospitals
# that can handle it after a disaster
HAZEL_disaster_capacity_multiplier = 1.0
#Used in Hospital.cc to determine how many days after disaster
# before mobile vans will activate and when they will close
HAZEL_mobile_van_open_delay = 0
HAZEL_mobile_van_closure_day = 0
#Used in Place_List.cc to handle evacuations and
# Mobile van activation
HAZEL_disaster_start_sim_day = -1
HAZEL_disaster_end_sim_day = -1
HAZEL_disaster_capacity_multiplier = 1.0
HAZEL_disaster_evac_start_offset = 0
HAZEL_disaster_evac_end_offset = 0
# Households that evacuated begin returning
# on HAZEL_disaster_end_sim_day + HAZEL_disaster_return_start_offset
# Households that evacuated stop returning (they should all be back)
# on HAZEL_disaster_end_sim_day + HAZEL_disaster_return_end_offset
HAZEL_disaster_return_start_offset = 0
HAZEL_disaster_return_end_offset = 0
HAZEL_disaster_evac_prob_per_day = 0.0
HAZEL_disaster_return_prob_per_day = 0.0
HAZEL_mobile_van_max = 0
#Used in Activites.cc to boost seeking healthcare immediately after disaster
HAZEL_seek_hc_ramp_up_days = 0
HAZEL_seek_hc_ramp_up_mult = 1.0
##########################################################
#
# DISEASE PARAMETERS
#
##########################################################
# Number of diseases circulating
diseases = 1
# Names of diseases
disease_names = 1 influenza
### DISEASE TRANSMISSION MODE
influenza_transmission_mode = respiratory
### NATURAL HISTORY MODEL
# basic => on-off infectiousness and symptoms
influenza_natural_history_model = basic
##########################################################
#
# INFLUENZA PARAMETERS
#
##########################################################
### REQUIRED NATURAL HISTORY PARAMETERS
### transmission coefficient:
influenza_transmissibility = 1.0
### symptomatic rate:
influenza_probability_of_symptoms = 0.67
### optional symptoms age map
influenza_prob_symptoms_age_groups = 0
influenza_prob_symptoms_values = 0
### multiplier for asymptomatic infectivity
influenza_asymp_infectivity = 0.5
# Note: incubation refers to symptoms; latency refers to infectiousness
#### default: lognormal incubation and symptoms duration
influenza_symptoms_distributions = lognormal
influenza_incubation_period_median = 1.9
influenza_incubation_period_dispersion = 1.51
influenza_symptoms_duration_median = 5.0
influenza_symptoms_duration_dispersion = 1.5
#### default: infectiousness is concurrent with symptoms duration
influenza_infectious_distributions = offset_from_symptoms
influenza_infectious_start_offset = 0
influenza_infectious_end_offset = 0
#### default: binary symptoms and infectivity
influenza_full_symptoms_start = 0.0
influenza_full_symptoms_end = 1.0
influenza_full_infectivity_start = 0.0
influenza_full_infectivity_end = 1.0
######################################################
#
### DEFAULT PLACE-SPECIFIC CONTACTS FOR RESPIRATORY TRANSMISSION
#
# The following contacts are calibrated for Allegheny County for a 33%
# clinical attack rate using the default influenza model in this
# file. If you change any of the default parameters, make sure to
# recalibrate these values.
#
### 2010 v1 calibration for Allegheny County, PA (FIPS=42003)
R0_a = 0.0440073
R0_b = 0.485761
R0 = -1.0
### CALIBRATION PHASE I STEP 1 at Thu Dec 3 12:08:18 2015
### runs = 20 cores = 4
household_contacts = 0.1402
neighborhood_contacts = 0.7883
school_contacts = 0.6295
workplace_contacts = 0.0686
classroom_contacts = 1.2590
office_contacts = 0.1372
enable_transmission_bias = 1
neighborhood_same_age_bias = 0.1
# community contacts increase on weekends
weekend_contact_rate = 1.5
##################### END default calibration parameters
### IMMUNITY
influenza_immunity_loss_rate = 0
influenza_residual_immunity_age_groups = 0
influenza_residual_immunity_values = 0
influenza_infection_immunity_age_groups = 1 120
influenza_infection_immunity_values = 1 1.0
Immunization = -1
### OPTIONAL FEATURES
influenza_infectivity_threshold = 0.0
influenza_symptomaticity_threshold = 0.0
### CASE FATALITY
influenza_enable_case_fatality = 0
influenza_min_symptoms_for_case_fatality = 1.0
influenza_case_fatality_age_groups = 1 120
influenza_case_fatality_values = 1 1.0
influenza_case_fatality_prob_by_day = 4 0.0000715 0.0000715 0.0000715 0.0000715
### AT-RISK POPULATIONS:
# influenza_at_risk_age_groups = 7 2 5 19 25 50 65 120
# influenza_at_risk_values = 7 0.039 0.0883 0.1168 0.1235 0.1570 0.3056 0.4701
influenza_at_risk_age_groups = 0
influenza_at_risk_values = 0
### HOSPITALIZATION:
#influenza_hospitalization_prob_age_groups = 7 2 5 19 25 50 65 120
#influenza_hospitalization_prob_values = 7 0.01 0.01 0.01 0.01 0.01 0.01 0.01
#influenza_outpatient_healthcare_prob_age_groups = 7 2 5 19 25 50 65 120
#influenza_outpatient_healthcare_prob_values = 7 0.03 0.03 0.03 0.03 0.03 0.03 0.03
influenza_min_symptoms_for_seek_healthcare = 1.0
influenza_hospitalization_prob_age_groups = 0
influenza_hospitalization_prob_values = 0
influenza_outpatient_healthcare_prob_age_groups = 0
influenza_outpatient_healthcare_prob_values = 0
### PLACE-SPECIFIC TRANSMISSION MATRICES (old values)
# groups = children adults
household_trans_per_contact = 4 0.6 0.3 0.3 0.4
# groups = children adults
neighborhood_trans_per_contact = 4 0.0048 0.0048 0.0048 0.0048
# groups = adult_workers
workplace_trans_per_contact = 1 0.0575
office_trans_per_contact = 1 0.0575
# groups = HCWs Patients Visitors
hospital_trans_per_contact = 9 0.0575 0.115 0.0575 0.115 0.0575 0.115 0.0575 0.115 0.0575
# groups = elem_students mid_students high_students teachers
school_trans_per_contact = 16 0.0435 0 0 0.0435 0 0.0375 0 0.0375 0 0 0.0315 0.0315 0.0435 0.0375 0.0315 0.0575
classroom_trans_per_contact = 16 0.0435 0 0 0.0435 0 0.0375 0 0.0375 0 0 0.0315 0.0315 0.0435 0.0375 0.0315 0.0575
###
### PLACE-SPECIFIC TRANSMISSION MATRICES (deprecated)
household_trans_per_contact = 4 1.0 0.5 0.5 0.67
neighborhood_trans_per_contact = 4 1.0 0.5 0.5 1.0
workplace_trans_per_contact = 1 1.0
office_trans_per_contact = 1 1.0
school_trans_per_contact = 16 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
classroom_trans_per_contact = 16 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
######### OPTIONAL EXPERIMENT_SPECIFIC PARAMETERS ######################
#########################################################
##
## The following parameter make neighborhhood transmission
## dependent on neighborhood density -- that is, the more
## people there are in a neighborhood, the greater the
## number of contacts per infectious person.
## This requires a recalibration.
##
enable_neighborhood_density_transmission = 0
enable_density_transmission_maximum_infectees = 1
density_transmission_maximum_infectees = 10
## experimental:
hospital_contacts = 0
##########################################################
#
# VECTOR TRANSMISSION MODEL (OPTIONAL)
#
##########################################################
enable_vector_layer = 0
enable_vector_transmission = 0
enable_vector_control = 0
school_vector_control = 0
workplace_vector_control = 0
household_vector_control = 0
neighborhood_vector_control = 0
limit_vector_control = 0
report_vector_population = 0
# size of vector patches in km
vector_patch_size = 1.0
vector_infection_efficiency = 0.2
vector_transmission_efficiency = 0.3
##########################################################
#
# VIRAL EVOLUTION (OPTIONAL)
#
##########################################################
enable_viral_evolution = 0
all_diseases_antigenically_identical = 0
influenza_evolution = 0
influenza_num_strains = 1
influenza_strain_data[0] = a:1
enable_protection = 0
enable_residual_immunity_by_FIPS = 0
track_residual_immunity = 0
track_multi_strain_stats = 0
num_codons = 12
codon_translation_file = $FRED_HOME/input_files/evolution/numAA.txt
past_infections_strains_file = none
past_infections_hosts_file = none
# evolution visualization stuff
prevalencefile = none
# prevalencefile = prev.txt
incidencefile = none
# incidencefile = incd.txt
immunity_file = none
# immunity_file = immunityd.txt
transmissionsfile = none
# transmissionsfile = transmissionsd.txt
strainsfile = none
# strainsfile = strainsd.txt
##########################################################
#
# PERSONAL HYGIENE BEHAVIORS (OPTIONAL)
#
##########################################################
enable_face_mask_usage = 0
face_mask_compliance = 0
days_to_wear_face_masks = 2
influenza_face_mask_transmission_efficacy = 0.26
influenza_face_mask_susceptibility_efficacy = 0.26
enable_hand_washing = 0
hand_washing_compliance = 0
influenza_hand_washing_transmission_efficacy = 0.21
influenza_hand_washing_susceptibility_efficacy = 0.21
influenza_face_mask_plus_hand_washing_transmission_efficacy = 0.33
influenza_face_mask_plus_hand_washing_susceptibility_efficacy = 0.33
##########################################################
#
# SEASONALITY (OPTIONAL)
#
##########################################################
# The following parameters provide a sinusoidal reduction in
# transmission in all diseases, with a peak tranmissibility of the given
# day of the year, and the minimum transmissibility 183 days before or
# after the peak.
#
# The day_of_year with maximum seasonal transmissibility (e.g. Jan 1 -> day_of_year = 1)
seasonal_peak_day_of_year = 1
#
# Amount of reduction in seasonal transmissibility (default = 0 -> no seasonal change).
# Note: If set to a value > 1, then transmission will be 0 for part of the year.
seasonal_reduction = 0
#########
# the following use a climate model to affect seasonality (experimental)
enable_seasonality = 0
enable_climate = 0
seasonality_timestep_file = none
# seasonality_timestep_file = $FRED_HOME/input_files/countries/usa/seasonality_timestep
influenza_seasonality_multiplier_max = 1
influenza_seasonality_multiplier_min = 1
influenza_seasonality_multiplier_Ka = -180
#########
##########################################################
#
# SHELTER IN PLACE (OPTIONAL)
#
##########################################################
enable_shelter_in_place = 0
shelter_in_place_duration_mean = 0
shelter_in_place_duration_std = 0.0
shelter_in_place_delay_mean = 0
shelter_in_place_delay_std = 0
shelter_in_place_compliance = 0
shelter_in_place_early_rate = 0.0
shelter_in_place_decay_rate = 0.0
shelter_in_place_by_income = 0
##########################################################
#
# POPULATION DYNAMICS (OPTIONAL)
#
##########################################################
enable_population_dynamics = 0
mortality_rate_file = $FRED_HOME/input_files/countries/usa/mortality_rates.txt
mortality_rate_multiplier = 1
mortality_rate_adjustment_weight = 0
birth_rate_file = $FRED_HOME/input_files/countries/usa/birth_rates.txt
birth_rate_multiplier = 1
# this the percent per year population growth, e.g. 1 => 1% growth each year
population_growth_rate = 0
# assume a mean of 4 years in college
college_departure_rate = 0.25
# assume a mean of 4 years enlistment
military_departure_rate = 0.25
# assume a mean of 2 years: http://www.bjs.gov/content/pub/pdf/p10.pdf
prison_departure_rate = 0.5
# assuming mean age of leaving home about 21
youth_home_departure_rate = 0.3
adult_home_departure_rate = 0.3
##########################################################
#
# CHRONIC CONDITIONS (OPTIONAL)
#
##########################################################
enable_chronic_condition = 0
# If chronic conditions are enabled, then the age map data for the
# various conditions will be used
asthma_prob_age_groups = 0
asthma_prob_values = 0
COPD_prob_age_groups = 0
COPD_prob_values = 0
chronic_renal_disease_prob_age_groups = 0
chronic_renal_disease_prob_values = 0
diabetes_prob_age_groups = 0
diabetes_prob_values = 0
heart_disease_prob_age_groups = 0
heart_disease_prob_values = 0
hypertension_prob_age_groups = 0
hypertension_prob_values = 0
hypercholestrolemia_prob_age_groups = 0
hypercholestrolemia_prob_values = 0
# Chronic condition hospitalization multipliers
asthma_hospitalization_prob_mult_age_groups = 1 120
asthma_hospitalization_prob_mult_values = 1 1.0
COPD_hospitalization_prob_mult_age_groups = 1 120
COPD_hospitalization_prob_mult_values = 1 1.0
chronic_renal_disease_hospitalization_prob_mult_age_groups = 1 120
chronic_renal_disease_hospitalization_prob_mult_values = 1 1.0
diabetes_hospitalization_prob_mult_age_groups = 1 120
diabetes_hospitalization_prob_mult_values = 1 1.0
heart_disease_hospitalization_prob_mult_age_groups = 1 120
heart_disease_hospitalization_prob_mult_values = 1 1.0
hypertension_hospitalization_prob_mult_age_groups = 1 120
hypertension_hospitalization_prob_mult_values = 1 1.0
hypercholestrolemia_hospitalization_prob_mult_age_groups = 1 120
hypercholestrolemia_hospitalization_prob_mult_values = 1 1.0
pregnancy_hospitalization_prob_mult_age_groups = 1 120
pregnancy_hospitalization_prob_mult_values = 1 1.0
# Chronic condition case fatality multipliers
asthma_case_fatality_prob_mult_age_groups = 1 120
asthma_case_fatality_prob_mult_values = 1 1.0
COPD_case_fatality_prob_mult_age_groups = 1 120
COPD_case_fatality_prob_mult_values = 1 1.0
chronic_renal_disease_case_fatality_prob_mult_age_groups = 1 120
chronic_renal_disease_case_fatality_prob_mult_values = 1 1.0
diabetes_case_fatality_prob_mult_age_groups = 1 120
diabetes_case_fatality_prob_mult_values = 1 1.0
heart_disease_case_fatality_prob_mult_age_groups = 1 120
heart_disease_case_fatality_prob_mult_values = 1 1.0
hypertension_case_fatality_prob_mult_age_groups = 1 120
hypertension_case_fatality_prob_mult_values = 1 1.0
hypercholestrolemia_case_fatality_prob_mult_age_groups = 1 120
hypercholestrolemia_case_fatality_prob_mult_values = 1 1.0
pregnancy_case_fatality_prob_mult_age_groups = 1 120
pregnancy_case_fatality_prob_mult_values = 1 1.0
##########################################################
#
# INCOME BASED SUSCEPTIBILITY MODIFIER PARAMETERS (OPTIONAL)
#
##########################################################
enable_hh_income_based_susc_mod = 0;
hh_income_susc_mod_floor = 0.0
##########################################################
#
# BEHAVIORAL PARAMETERS (OPTIONAL)
#
##########################################################
#### INDIVIDUAL BEHAVIORS (EXPERIMENTAL!)
enable_behaviors = 0
stay_home_when_sick_enabled = 0
keep_child_home_when_sick_enabled = 0
accept_vaccine_enabled = 0
accept_vaccine_dose_enabled = 0
accept_vaccine_for_child_enabled = 0
accept_vaccine_dose_for_child_enabled = 0
# let individuals decide whether to take sick leave if available
take_sick_leave_enabled = 0
#### PERCEPTION (EXPERIMENTAL)
memory_decay = 2 0 0
#### SICK LEAVE BEHAVIOR
## default sick behavior:
# if set, use sick_day_prob and overide other sick leave behavior
enable_default_sick_behavior = 1
# each agent withdraws to the household with sick_day_prob
# on each day the agent is symptomatic
sick_day_prob = 0.5
# Determine how sick leave is given
# Don’t use default_sick_behavior
# 1 - By Workplace Size
# 2 - By Household Income Quartile
sick_leave_dist_method = 1
wp_size_sl_prob_vec = 0
hh_income_qtile_sl_prob_vec = 0
wp_small_mean_sl_days_available = 0.0
wp_large_mean_sl_days_available = 0.0
wp_size_cutoff_sl_exception = 0
## behavior if sick leave is available (SLA)
SLA_absent_prob = 0.72
## behavior if sick leave is unavailable (SLU)
SLU_absent_prob = 0.52
work_absenteeism = 0.0
school_absenteeism = 0.0
standard_sicktime_allocated_per_child = 0.0
#### BEHAVIOR MARKET SEGMENTS (EXPERIMENTAL!)
#
# BEHAVIOR_CHANGE_MODEL 0 = ALWAYS REFUSE
# BEHAVIOR_CHANGE_MODEL 1 = ALWAYS ACCEPT