import pandas as pd
from collections import defaultdict
from abc import ABC, abstractmethod
from typing import Dict, Union
import pathlib
from importlib import resources
from importlib.abc import Traversable
import emodpy_hiv.campaign.coc as coc
import emod_api
import emod_api.campaign as camp
import emodpy_hiv.country_data.zambia as zambia_data
[docs]class DefaultZambiaData:
"""
This class contains the default data file paths for Zambia.
"""
data_root = resources.files(zambia_data)
historical_vmmc_data_file = data_root.joinpath("historical_vmmc_data.csv")
initial_age_distribution_file = data_root.joinpath("initial_age_distribution.csv")
initial_population_file = data_root.joinpath("initial_population.csv")
female_mortality_file = data_root.joinpath("parsed_mortality--female.csv")
male_mortality_file = data_root.joinpath("parsed_mortality--male.csv")
fertility_file = data_root.joinpath("parsed_fertility.csv")
[docs]class Country(ABC):
def __init__(self,
country_name: str, base_year: float = 1960.5):
self.country_name = country_name
self.base_year = base_year
[docs] def initialize_campaign(self, schema_path, base_year):
print(f"Telling emod-api to use {schema_path} as schema.")
camp.set_schema(schema_path)
camp.base_year = base_year
return camp
[docs] @staticmethod
def load_nchooser_distribution_data(file_path: str) -> dict:
"""
Load target distribution data into a dataframe format that works for Nchooser and group by node_id.
Args:
file_path: path to the csv file that contains the data
Returns:
dict: a dictionary that has node_id as the key and a dataframe as the value. The dataframe contains the
distribution data for Nchooser.
"""
data = pd.read_csv(file_path)
data_dict = defaultdict(pd.DataFrame)
for node_id in pd.unique(data['node_set']):
node_data = data[data['node_set'] == node_id]
node_dict = {'year': [], 'min_age': [], 'max_age': [], 'n_circumcisions': []}
for year in pd.unique(node_data['year']):
year_data = node_data[node_data['year'] == year]
for _, row in year_data.iterrows():
node_dict['year'].append(year)
node_dict['min_age'].append(float(row['age_bin'].split(':')[0].replace('[', '')))
node_dict['max_age'].append(float(row['age_bin'].split(':')[1].replace(')', '')) - 0.00001)
node_dict['n_circumcisions'].append(int(row['n_circumcisions']))
data_dict[int(node_id)] = pd.DataFrame(node_dict)
return data_dict
[docs] def add_male_circumcision(self, campaign, historical_vmmc_data_filepath,
traditional_male_circumcision_coverages_per_node):
# ---- Add historical vmmc NChooser events to campaign ----
self.add_historical_vmmc_nchooser(campaign, historical_vmmc_data_filepath)
# ---- Add traditional male circumcision to campaign ----
self.add_traditional_male_circumcision(campaign, traditional_male_circumcision_coverages_per_node)
# ---- Add (VMMC) reference tracking to campaign ----
self.add_vmmc_reference_tracking(campaign)
[docs] def add_historical_vmmc_nchooser(self, campaign, historical_vmmc_data_filepath: str,
historical_vmmc_reduced_acquire=0.6, historical_vmmc_start_year=2008):
# Load historical vmmc data into data frame
historical_vmmc_data = self.load_nchooser_distribution_data(historical_vmmc_data_filepath)
for node_id in historical_vmmc_data:
coc.add_historical_vmmc_nchooser(campaign=campaign,
historical_vmmc_distributions_by_time=historical_vmmc_data[node_id],
historical_vmmc_reduced_acquire=historical_vmmc_reduced_acquire,
historical_vmmc_property_restrictions=None,
historical_vmmc_start_year=historical_vmmc_start_year,
historical_vmmc_node_ids=[node_id],
has_intervention_name_exclusion=coc.ANY_MC,
event_name=f"NChooser to produce specified number of program VMMCs in node"
f" {node_id}, {self.country_name}")
[docs] def add_traditional_male_circumcision(self, campaign, traditional_male_circumcision_coverages_per_node: dict):
randomchoice_start_year = 1975
traditional_male_circumcision_start_year = 1961
traditional_male_circumcision_reduced_acquire = 0.6
for node_id, mc_coverage in traditional_male_circumcision_coverages_per_node.items():
coc.add_traditional_male_circumcision(campaign=campaign,
traditional_male_circumcision_start_year=traditional_male_circumcision_start_year,
randomchoice_start_year=randomchoice_start_year,
traditional_male_circumcision_coverage=mc_coverage,
traditional_male_circumcision_reduced_acquire=traditional_male_circumcision_reduced_acquire,
traditional_male_circumcision_node_ids=[node_id])
[docs] def add_vmmc_reference_tracking(self, campaign):
vmmc_time_value_map = {"Times": [2015.9999, 2016, 2021], "Values": [0, 0.54, 0.9]}
coc.add_vmmc_reference_tracking(campaign=campaign,
vmmc_time_value_map=vmmc_time_value_map,
vmmc_reduced_acquire=0.6,
vmmc_target_min_age=15,
vmmc_target_max_age=29.999999,
vmmc_start_year=2015,
vmmc_node_ids=None, # all nodes
update_period=30.4166666666667)
[docs] def seed_infections(self, campaign):
# add outbreak individual interventions with coverage 0.1 to all nodes with Risk:HIGH
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# DMB Removing "Risk:HIGH" and increasing coverage to 0.2. Anna had some plots from 2016
# that showed prevalence closer to 20% for gender-based age bins 25-45
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
coc.seed_infections(campaign=campaign,
seeding_node_ids=None, # all nodes
seeding_start_year=1982,
seeding_coverage=0.2,
seeding_target_property_restrictions=[])
[docs] def add_csw(self, campaign):
csw_male_uptake_coverage_nodeset1 = 0.195
csw_female_uptake_coverage_nodeset1 = 0.125
csw_male_uptake_coverage_nodeset2 = 0.0781
csw_female_uptake_coverage_nodeset2 = 0.05
coc.add_csw(campaign=campaign,
node_ids=[1, 2, 3, 7, 9],
male_uptake_coverage=csw_male_uptake_coverage_nodeset1,
female_uptake_coverage=csw_female_uptake_coverage_nodeset1)
coc.add_csw(campaign=campaign,
node_ids=[4, 5, 6, 8, 10],
male_uptake_coverage=csw_male_uptake_coverage_nodeset2,
female_uptake_coverage=csw_female_uptake_coverage_nodeset2)
[docs] def add_post_debut_coinfection(self, campaign):
coinfection_risk_to_coverage = {'HIGH': 0.3, 'MEDIUM': 0.3, 'LOW': 0.1}
for risk, coverage in coinfection_risk_to_coverage.items():
coc.add_post_debut_coinfection(campaign=campaign,
coinfection_coverage=coverage,
coinfection_gender='All',
coinfection_IP=f'Risk:{risk}')
[docs] def add_pmtct(self, campaign):
self.add_state_TestingOnANC(campaign)
# Testing of children at 6 weeks of age
self.add_state_TestingOnChild6w(campaign)
[docs] def add_health_care_testing(self,
campaign: emod_api.campaign,
hct_start_year: float = 1990):
start_day = coc.timestep_from_year(hct_start_year, self.base_year)
self.add_state_HCTUptakeAtDebut(campaign=campaign,
start_day=start_day)
self.add_state_HCTUptakePostDebut(campaign=campaign,
start_day=start_day)
self.add_state_HCTTestingLoop(campaign=campaign,
start_day=start_day)
[docs] def add_ART_cascade(self, campaign, art_cascade_start_year: float = 1990):
start_day = coc.timestep_from_year(art_cascade_start_year, self.base_year)
self.add_state_TestingOnSymptomatic(campaign=campaign, start_day=start_day)
#
# ---- BEGIN ART STAGING SECTION ----
#
self.add_state_ARTStagingDiagnosticTest(campaign=campaign, start_day=start_day)
self.add_state_ARTStaging(campaign=campaign, start_day=start_day)
#
# ---- BEGIN PRE-ART ----
#
# chance of linking to pre-ART
self.add_state_LinkingToPreART(campaign=campaign, start_day=start_day)
# ensuring each agent continues this cascade once per timestep
self.add_state_OnPreART(campaign=campaign, start_day=start_day)
#
# ---- BEGIN ART LINKING ----
#
self.add_state_LinkingToART(campaign=campaign, start_day=start_day)
# decide to initiate ART now or later
self.add_state_OnART(campaign=campaign, start_day=start_day)
#
self.add_state_LostForever(campaign=campaign, start_day=start_day)
[docs] def add_state_TestingOnANC(self, campaign: emod_api.campaign):
node_ids = None
start_year = 1990
coverage = 1.0
sigmoid_ramp_min = 0
sigmoid_ramp_max = 0.975
sigmoid_ramp_midyear = 2005.87
sigmoid_ramp_rate = 0.7136
link_to_ART_rate = 0.8
treatment_a_efficacy = 0.9
treatment_b_efficacy = 0.96667
sdNVP_efficacy = 0.66
start_day = coc.timestep_from_year(start_year, campaign.base_year)
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART,
coc.CascadeState.ART_STAGING,
coc.CascadeState.TESTING_ON_SYMPTOMATIC]
property_restrictions = 'Accessibility:Yes'
coc.add_state_TestingOnANC(campaign=campaign,
disqualifying_properties=disqualifying_properties,
coverage=coverage,
link_to_ART_rate=link_to_ART_rate,
node_ids=node_ids,
sigmoid_ramp_max=sigmoid_ramp_max,
sigmoid_ramp_midyear=sigmoid_ramp_midyear,
sigmoid_ramp_min=sigmoid_ramp_min,
sigmoid_ramp_rate=sigmoid_ramp_rate,
treatment_a_efficacy=treatment_a_efficacy,
treatment_b_efficacy=treatment_b_efficacy,
sdNVP_efficacy=sdNVP_efficacy,
start_day=start_day,
property_restrictions=property_restrictions)
[docs] def add_state_TestingOnChild6w(self, campaign: emod_api.campaign):
child_testing_start_year = 2004
node_ids = None
child_testing_time_value_map = {"Times": [2004, 2005, 2006, 2008, 2009],
"Values": [0, 0.03, 0.1, 0.2, 0.3365]}
child_testing_start_day = coc.timestep_from_year(child_testing_start_year, campaign.base_year)
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART,
coc.CascadeState.ART_STAGING,
coc.CascadeState.TESTING_ON_SYMPTOMATIC]
property_restrictions = 'Accessibility:Yes'
coc.add_state_TestingOnChild6w(campaign=campaign,
disqualifying_properties=disqualifying_properties,
time_value_map=child_testing_time_value_map,
node_ids=node_ids,
property_restrictions=property_restrictions,
start_day=child_testing_start_day)
[docs] def add_state_HCTUptakeAtDebut(self, campaign: emod_api.campaign, start_day: int):
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART,
coc.CascadeState.ART_STAGING]
coc.add_state_HCTUptakeAtDebut(campaign=campaign,
disqualifying_properties=disqualifying_properties,
node_ids=None,
start_day=start_day)
[docs] def add_state_HCTUptakePostDebut(self, campaign: emod_api.campaign, start_day: int):
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART,
coc.CascadeState.ART_STAGING]
coc.add_state_HCTUptakePostDebut(campaign=campaign, disqualifying_properties=disqualifying_properties,
node_ids=None, hct_reentry_rate=1, start_day=start_day,
tvmap_test_for_enter_HCT_testing_loop=coc.all_negative_time_value_map)
[docs] def add_state_HCTTestingLoop(self, campaign: emod_api.campaign, start_day: int):
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART,
coc.CascadeState.ART_STAGING]
hct_delay_to_next_test = [730, 365, 1100] # Default values for Zambia model
hct_delay_to_next_test_node_ids = [[1, 2, 3, 4, 6, 7], [5, 9, 10], [8]] # Default values for Zambia model
hct_delay_to_next_test_node_names = ['Default', 'Lusaka, Southern, Western',
'Northern'] # Default values for Zambia model
coc.add_state_HCTTestingLoop(campaign=campaign, disqualifying_properties=disqualifying_properties,
start_day=start_day, hct_retention_rate=0.95,
tvmap_consider_immediate_ART=coc.all_negative_time_value_map,
hct_delay_to_next_test=hct_delay_to_next_test,
hct_delay_to_next_test_node_ids=hct_delay_to_next_test_node_ids,
hct_delay_to_next_test_node_names=hct_delay_to_next_test_node_names, node_ids=None)
[docs] def add_state_TestingOnSymptomatic(self, campaign: emod_api.campaign, start_day: int):
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART,
coc.CascadeState.ART_STAGING]
coc.add_state_TestingOnSymptomatic(campaign=campaign,
disqualifying_properties=disqualifying_properties,
start_day=start_day, node_ids=None,
tvmap_increased_symptomatic_presentation=coc.all_negative_time_value_map)
[docs] def add_state_ARTStagingDiagnosticTest(self, campaign: emod_api.campaign, start_day: int):
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART]
coc.add_state_ARTStagingDiagnosticTest(campaign=campaign,
disqualifying_properties=disqualifying_properties,
start_day=start_day, node_ids=None)
[docs] def add_state_ARTStaging(self, campaign: emod_api.campaign, start_day: int):
cd4_retention_rate = 1
pre_staging_retention = 0.85
disqualifying_properties = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART,
coc.CascadeState.LINKING_TO_PRE_ART]
coc.add_state_ARTStaging(campaign=campaign,
disqualifying_properties=disqualifying_properties,
start_day=start_day, node_ids=None, pre_staging_retention=pre_staging_retention,
cd4_retention_rate=cd4_retention_rate)
[docs] def add_state_LinkingToPreART(self, campaign: emod_api.campaign, start_day: int):
disqualifying_properties_pre_art_linking = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART,
coc.CascadeState.ON_PRE_ART]
coc.add_state_LinkingToPreART(campaign=campaign,
disqualifying_properties=disqualifying_properties_pre_art_linking,
start_day=start_day, node_ids=None)
[docs] def add_state_OnPreART(self, campaign: emod_api.campaign, start_day: int):
disqualifying_properties_pre_art = [coc.CascadeState.LOST_FOREVER,
coc.CascadeState.ON_ART,
coc.CascadeState.LINKING_TO_ART]
pre_art_retention = 0.75
coc.add_state_OnPreART(campaign=campaign,
node_ids=None,
pre_art_retention=pre_art_retention,
disqualifying_properties=disqualifying_properties_pre_art,
start_day=start_day)
[docs] def add_state_LinkingToART(self, campaign: emod_api.campaign, start_day: int):
node_ids = None
disqualifying_properties = [coc.CascadeState.LOST_FOREVER, coc.CascadeState.ON_ART]
coc.add_state_LinkingToART(campaign=campaign,
node_ids=node_ids,
disqualifying_properties=disqualifying_properties,
start_day=start_day)
[docs] def add_state_OnART(self, campaign: emod_api.campaign, start_day: int):
node_ids = None
art_reenrollment_willingness = 0.9
immediate_art_rate = 0.1
tvmap_immediate_ART_restart = coc.all_negative_time_value_map
tvmap_reconsider_lost_forever = coc.all_negative_time_value_map
disqualifying_properties = [coc.CascadeState.LOST_FOREVER]
coc.add_state_OnART(campaign=campaign,
art_reenrollment_willingness=art_reenrollment_willingness,
immediate_art_rate=immediate_art_rate,
node_ids=node_ids,
disqualifying_properties=disqualifying_properties,
start_day=start_day,
tvmap_immediate_ART_restart=tvmap_immediate_ART_restart,
tvmap_reconsider_lost_forever=tvmap_reconsider_lost_forever)
[docs] def add_state_LostForever(self, campaign: emod_api.campaign, start_day: int):
node_ids = None
coc.add_state_LostForever(campaign=campaign,
node_ids=node_ids,
start_day=start_day)
[docs] def build_campaign(self,
schema_path: str,
base_year: float,
historical_vmmc_data_filepath: Union[str, Traversable],
traditional_male_circumcision_coverages_per_node: dict):
"""
A method to pass into EMODTask to build campaign events for Emod.
Args:
schema_path(str): file path to the schema file.
base_year(float): base year for the Simulation.
historical_vmmc_data_filepath(str): file path to the historical vmmc data csv file.
traditional_male_circumcision_coverages_per_node(Dict[int: float]): coverage of traditional male
circumcision per node.
Returns:
"""
# ---- Initialize a campaign object -----
campaign = self.initialize_campaign(schema_path, base_year)
# ---- Add male circumcision to campaign -----
# This block contains 3 chunk of events: historical vmmc NChooser, traditional male circumcision and VMMC with
# reference tracking.
self.add_male_circumcision(campaign, historical_vmmc_data_filepath,
traditional_male_circumcision_coverages_per_node)
# ---- Add prevention of mother-to-child transmission (PMTCT) to campaign ----
# This pmtct block contains 2 Cascade states: TestingOnANC and TestingOnChild6w
self.add_pmtct(campaign)
# ---- Add OutbreakIndividual interventions to campaign ----
self.seed_infections(campaign)
# ---- Add commercial sex worker (CSW) uptake and dropout (with delays) for men and women to campaign ----
self.add_csw(campaign)
# ---- Add co-infections post sexual debut to campaign ----
self.add_post_debut_coinfection(campaign)
# ---- Add health care testing loops to campaign ----
# This health care testing block contains 3 Cascade states: HCTUptakeAtDebut, HCTUptakePostDebut and
# HCTTestingLoop
self.add_health_care_testing(campaign)
# ---- Add Antiretroviral Therapy (ART) Cascade to campaign ----
# This ART Cascade block contains 8 Cascade states: TestingOnSymptomatic, ARTStagingDiagnosticTest, ARTStaging
# LinkingToPreART, OnPreART, LinkingToART, OnART and LostForever
self.add_ART_cascade(campaign)
return campaign
[docs]class Zambia(Country):
"""
Zambia country model class. This class is a subclass of the Country class and implements the build_campaign method
to build campaign object to be used with EMODTask.
Example:
>>> from emodpy_hiv.country_model import Zambia
>>> from emodpy.emod_task import EMODTask
>>> import manifest
>>> from functools import partial
>>> zambia = Zambia()
>>> # Define a campaign_builder function to pass in the required arguments for the zambia.build_campaign() method.
>>> def campaign_builder():
>>> return zambia.build_campaign(schema_path=manifest.schema_file, base_year=1960.5)
>>> # Alternatively, you can use the partial function to pass in the required arguments for the
>>> # zambia.build_campaign() method. For example:
>>> # campaign_builder = partial(zambia.build_campaign, schema_path=manifest.schema_file, base_year=1960.5)
>>> # Create an EMODTask object using the campaign_builder function
>>> task = EMODTask.from_default2(config_path="config.json",
>>> eradication_path=manifest.eradication_path,
>>> campaign_builder=campaign_builder, ...)
"""
def __init__(self, country_name="Zambia"):
super().__init__(country_name)
[docs] def build_config(self, config):
config.parameters.Simulation_Type = "HIV_SIM"
config.parameters.Simulation_Duration = 99.5 * 365
config.parameters.Simulation_Timestep = 30.4166666666667
# Reduce the initial population so tutorial can double it
config.parameters.x_Base_Population = 0.002 #0.005
config.parameters.Start_Time = 0
config.parameters.AIDS_Duration_In_Months = 9
config.parameters.AIDS_Stage_Infectivity_Multiplier = 4.5
config.parameters.Acute_Duration_In_Months = 2.9
config.parameters.Acute_Stage_Infectivity_Multiplier = 26
config.parameters.Age_Initialization_Distribution_Type = "DISTRIBUTION_COMPLEX"
config.parameters.Base_Individual_Sample_Rate = 1
config.parameters.Base_Infectivity = 0.00031382269992254885
config.parameters.Base_Year = 1960.5
config.parameters.Birth_Rate_Dependence = "INDIVIDUAL_PREGNANCIES_BY_AGE_AND_YEAR"
config.parameters.Birth_Rate_Time_Dependence = "NONE"
config.parameters.CD4_At_Death_LogLogistic_Heterogeneity = 0.7
config.parameters.CD4_At_Death_LogLogistic_Scale = 2.96
config.parameters.CD4_Post_Infection_Weibull_Heterogeneity = 0.2756
config.parameters.CD4_Post_Infection_Weibull_Scale = 560.43
config.parameters.Coital_Dilution_Factor_2_Partners = 0.75
config.parameters.Coital_Dilution_Factor_3_Partners = 0.6
config.parameters.Coital_Dilution_Factor_4_Plus_Partners = 0.45
config.parameters.Condom_Transmission_Blocking_Probability = 0.8
config.parameters.Days_Between_Symptomatic_And_Death_Weibull_Heterogeneity = 0.5
config.parameters.Days_Between_Symptomatic_And_Death_Weibull_Scale = 618.341625
config.parameters.Death_Rate_Dependence = "NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER"
config.parameters.Enable_Aging = 1
config.parameters.Enable_Birth = 1
config.parameters.Enable_Coital_Dilution = 1
config.parameters.Enable_Default_Reporting = 1
config.parameters.Enable_Demographics_Reporting = 0
config.parameters.Enable_Demographics_Birth = 0
config.parameters.Enable_Infectivity_Reservoir = 0
config.parameters.Enable_Maternal_Protection = 0
config.parameters.Enable_Natural_Mortality = 1
config.parameters.HIV_Adult_Survival_Scale_Parameter_Intercept = 21.182
config.parameters.HIV_Adult_Survival_Scale_Parameter_Slope = -0.2717
config.parameters.HIV_Adult_Survival_Shape_Parameter = 2
config.parameters.HIV_Age_Max_for_Adult_Age_Dependent_Survival = 50
config.parameters.HIV_Age_Max_for_Child_Survival_Function = 15
config.parameters.HIV_Child_Survival_Rapid_Progressor_Fraction = 0.57
config.parameters.HIV_Child_Survival_Rapid_Progressor_Rate = 1.52
config.parameters.HIV_Child_Survival_Slow_Progressor_Scale = 16
config.parameters.HIV_Child_Survival_Slow_Progressor_Shape = 2.7
config.parameters.Heterogeneous_Infectiousness_LogNormal_Scale = 0
config.parameters.Incubation_Period_Constant = 0
config.parameters.Incubation_Period_Distribution = "CONSTANT_DISTRIBUTION"
config.parameters.Individual_Sampling_Type = "FIXED_SAMPLING"
config.parameters.Infection_Updates_Per_Timestep = 1
config.parameters.Infectivity_Scale_Type = "CONSTANT_INFECTIVITY"
config.parameters.Male_To_Female_Relative_Infectivity_Ages = [0, 15, 25]
config.parameters.Male_To_Female_Relative_Infectivity_Multipliers = [2.9976868182763963,
2.9976868182763963,
2.936393464131044]
config.parameters.Maternal_Infection_Transmission_Probability = 0.3
config.parameters.Maternal_Transmission_ART_Multiplier = 0.03334
config.parameters.Migration_Model = "NO_MIGRATION"
config.parameters.Min_Days_Between_Adding_Relationships = 0
config.parameters.Node_Grid_Size = 0.009
config.parameters.PFA_Burnin_Duration_In_Days = 5475
config.parameters.PFA_Cum_Prob_Selection_Threshold = 0.2
config.parameters.Population_Density_Infectivity_Correction = "CONSTANT_INFECTIVITY"
config.parameters.Population_Scale_Type = "FIXED_SCALING"
config.parameters.Run_Number = 1
config.parameters.STI_Coinfection_Acquisition_Multiplier = 5.5
config.parameters.STI_Coinfection_Transmission_Multiplier = 5.5
config.parameters.Serialized_Population_Reading_Type = "NONE"
config.parameters.Serialized_Population_Writing_Type = "NONE"
config.parameters.Sexual_Debut_Age_Setting_Type = "WEIBULL"
config.parameters.Sexual_Debut_Age_Female_Weibull_Heterogeneity = 0.22002507694706103
config.parameters.Sexual_Debut_Age_Female_Weibull_Scale = 15.092122890359025
config.parameters.Sexual_Debut_Age_Male_Weibull_Heterogeneity = 0.1268087803455056
config.parameters.Sexual_Debut_Age_Male_Weibull_Scale = 15.582384534190258
config.parameters.Sexual_Debut_Age_Min = 13
config.parameters["logLevel_InfectionHIV"] = "ERROR"
config.parameters["logLevel_Instrumentation"] = "INFO"
config.parameters["logLevel_Memory"] = "INFO"
config.parameters["logLevel_OutbreakIndividual"] = "ERROR"
config.parameters["logLevel_Simulation"] = "INFO"
config.parameters["logLevel_SusceptibilityHIV"] = "ERROR"
config.parameters["logLevel_AntiretroviralTherapy"] = "ERROR"
config.parameters["logLevel_default"] = "WARNING"
return config
[docs] def build_campaign(self,
schema_path: str,
base_year: float = 1960.5,
historical_vmmc_data_filepath: Union[str, Traversable] = DefaultZambiaData.historical_vmmc_data_file,
traditional_male_circumcision_coverages_per_node: Dict[int, float] = None):
if traditional_male_circumcision_coverages_per_node is None:
traditional_male_circumcision_coverages_per_node = \
{1: 0.054978651, 2: 0.139462861, 3: 0.028676043, 4: 0.091349358,
5: 0.12318707, 6: 0.039308099, 7: 0.727917322, 8: 0.041105263,
9: 0.044388102, 10: 0.398239794}
return super().build_campaign(schema_path, base_year, historical_vmmc_data_filepath,
traditional_male_circumcision_coverages_per_node)
[docs] def build_demographics(self):
import emodpy_hiv.demographics.country_models as demog_cm
demog = demog_cm.load_country_model_demographics_default("zambia")
return demog
