from typing import List, Any, Dict, Union, Optional
import pandas as pd
from emod_api import campaign
from emod_api import schema_to_class as s2c
from emod_api.interventions import utils
from emodpy_hiv.interventions import malecirc
[docs]def new_target_distribution(
camp: campaign,
age_ranges_years: List[List[Union[float]]],
start_year: float,
end_year: float,
num_targeted_females: List[int],
num_targeted_males: List[int],
num_targeted: List[int] = None,
property_restrictions_within_node: List[Dict[str, Any]] = None,
target_disease_state: List[List[str]] = None,
target_disease_state_has_intervention_name: str = None
) -> Dict:
"""
Configures a new target distribution for a campaign, specifying when, to whom, and how many interventions are
distributed.
Args:
camp (campaign): The campaign object to which the target distribution will be
added.
age_ranges_years (List[List[Union[float]], List[Union[float]]]): A 2D list specifying age ranges for
qualifying individuals.It should contain 2 lists, the first
list is 'Max' age and the second list for 'Min' age.
start_year (float): The year to start distributing the intervention.
end_year (float): The year to stop distributing the intervention.
num_targeted_females (List[int]): The number of female individuals to target.
num_targeted_males (List[int]): The number of male individuals to target.
num_targeted (List[int], optional): The number of individuals to target with the intervention.
Default to None.
property_restrictions_within_node (List[Dict[str, Any]], optional): A list of property restrictions, each
represented as a dict with keys as property names and values
as property values. Default to None.
target_disease_state (List[List[str]], optional): A 2D list specifying targeted disease states. Default to None.
To qualify for the intervention, an individual must have only
one of the targeted disease states. An individual must have
all the disease states in the inner array. Possible values
are: HIV_Positive, HIV_Negative, Tested_Positive,
Tested_Negative, Male_Circumcision_Positive,
Male_Circumcision_Negative, Has_Intervention,
Not_Have_Intervention.
target_disease_state_has_intervention_name (str, optional): The name of the intervention to look for in
an individual when targeting specific disease states (
'Has_Intervention' or 'Not_Have_Intervention').
Default to None.
Returns:
A dictionary representing a target distribution configuration for the campaign.
Example:
>>> camp = emod_api.campaign
>>> age_ranges_years = [[0.99999, 14.9999999, 49.9999, 64.9999], # Max ages
>>> [0, 1, 15, 50]] # Min ages
>>> num_targeted_females = [0, 0, 0, 0]
>>> num_targeted_males = [0, 8064.777513, 25054.39959, 179.0207223]
>>> distribution = new_target_distribution(camp=camp,
>>> age_ranges_years=age_ranges_years,
>>> start_year=2010.0,
>>> end_year=2010.999,
>>> num_targeted_females=num_targeted_females,
>>> num_targeted_males=num_targeted_males,
>>> target_disease_state=[["Not_Have_Intervention"]],
>>> target_disease_state_has_intervention_name="DMPA_or_control")
"""
distribution = s2c.get_class_with_defaults("idmType:TargetedDistributionHIV", camp.schema_path)
# Check if age_ranges_years is a 2D list contains 2 lists with the same length
if len(age_ranges_years) != 2:
raise ValueError("The age_ranges_years must contains exactly 2 lists.")
else:
max_ages, min_ages = age_ranges_years
if not isinstance(max_ages, list) or not isinstance(min_ages, list) or len(max_ages) != len(min_ages):
raise ValueError("The age_ranges_years must contains 2 lists with the same length.")
# Get the total number of age ranges
num_of_age_ranges = len(max_ages)
# The num_targeted lists in the intervention should have the same length
if len(num_targeted_males) != len(num_targeted_females) != num_of_age_ranges:
raise ValueError("num_targeted_males and num_targeted_females should have the same length "
"as age ranges.")
if num_targeted and len(num_targeted) != num_of_age_ranges:
raise ValueError("num_targeted should have the same length as age ranges.")
if start_year > end_year:
raise ValueError(f"start_year: {start_year} should be less than end_year: {end_year}.")
# Turn the age_ranges_years into a list of dictionaries each has a 'Min' and 'Max' age.
age_ranges_years_list = list()
for max_age, min_age in zip(max_ages, min_ages):
if max_age < min_age:
raise ValueError(f"Max age: {max_age} should be larger than min age: {min_age}.")
age_ranges_years_list.append({"Max": float(max_age),
"Min": float(min_age)})
distribution.Age_Ranges_Years = age_ranges_years_list
distribution.End_Year = float(end_year)
distribution.Start_Year = float(start_year)
distribution.Num_Targeted_Females = num_targeted_females.copy()
distribution.Num_Targeted_Males = num_targeted_males.copy()
if num_targeted:
distribution.Num_Targeted = num_targeted.copy()
if property_restrictions_within_node:
distribution.Property_Restrictions_Within_Node = property_restrictions_within_node.copy()
# Update distribution target disease state:
if target_disease_state:
distribution.Target_Disease_State = target_disease_state.copy()
intervention_states = ['Has_Intervention', 'Not_Have_Intervention']
# Check for the presence of intervention states: 'Has_Intervention' or 'Not_Have_Intervention'
contains_intervention = any(state in row for row in target_disease_state for state in intervention_states)
if contains_intervention and not target_disease_state_has_intervention_name:
raise ValueError("If using 'Has_Intervention' or 'Not_Have_Intervention' in Target_Disease_State, "
"you must also define 'Target_Disease_State_Has_Intervention_Name' to the name of"
" the intervention.")
elif not contains_intervention and target_disease_state_has_intervention_name:
raise ValueError("You are setting 'Target_Disease_State_Has_Intervention_Name' but Target_Disease_State"
" has no 'Has_Intervention' or 'Not_Have_Intervention'.")
elif contains_intervention:
distribution.Target_Disease_State_Has_Intervention_Name = target_disease_state_has_intervention_name
elif target_disease_state_has_intervention_name:
raise ValueError(
"You are setting 'Target_Disease_State_Has_Intervention_Name' but Target_Disease_State is empty.")
return distribution
[docs]def add_nchooser_event(
camp: campaign,
target_distributions: list,
intervention_config,
start_day: Union[int, float] = 1,
event_name: str = '',
node_ids: Optional[List[int]] = None
) -> None:
"""
Creates a new NChooserEventCoordinatorHIV event in the specified EMOD campaign.
This function adds a new event to an EMOD campaign object. It configures the event with specific
intervention configurations and distributions, targeting specific nodes from a defined start year.
Please refer to the documentation for NChooserEventCoordinatorHIV at the following link:
:doc:`emod-hiv:parameter-campaign-event-nchoosereventcoordinatorhiv`
Args:
camp (emod_api.campaign): The campaign object to which the event will be added. This
should be an instance of the emod_api.campaign class.
target_distributions (list[TargetedDistributionHIV]): A list of distribution configurations for the event. Each
distribution configuration dictates how the intervention
is distributed among the population.
intervention_config (Intervention): An Emod Intervention object.
start_day (int | float, optional): The day when the event starts. Defaults to 1.
event_name (str, optional): The name of the campaign event, default to empty string.
node_ids (list of int, optional): A list of node IDs where the event will be applied. If
None, the event applies to all nodes. Defaults to None.
Returns:
None: This function does not return anything. It modifies the campaign object in place.
Examples:
>>> import emod_api
>>> from emodpy_hiv.interventions import malecirc, nchooser
>>>
>>> campaign_obj = emod_api.campaign
>>> intervention_config = malecirc.new_intervention(campaign_obj)
>>> distribution_1 = nchooser.new_target_distribution(campaign_obj, ...)
>>> distribution_2 = nchooser.new_target_distribution(campaign_obj, ...)
>>> distributions = [distribution_1, distribution_2]
>>> nchooser.add_nchooser_event(campaign_obj, distributions, intervention_config, start_day=365,
>>> node_ids=[1, 2, 3])
"""
# NChooster Coordinator
nchooser = s2c.get_class_with_defaults("NChooserEventCoordinatorHIV", camp.schema_path)
if not target_distributions or not intervention_config:
raise ValueError("target_distributions and intervention_config should not be empty.")
nchooser.Distributions = target_distributions
nchooser.Intervention_Config = intervention_config
# Event
event = s2c.get_class_with_defaults("CampaignEvent", camp.schema_path)
event.Event_Coordinator_Config = nchooser
event.Start_Day = float(start_day)
event.Nodeset_Config = utils.do_nodes(camp.schema_path, node_ids)
if event_name:
# event.Event_Name = event_name
event['Event_Name'] = event_name # Event_Name is not in Schema
camp.add(event)
[docs]def add_nchooser_distributed_circumcision_event(camp: campaign,
target_disease_state: list,
has_intervention_name_exclusion: str,
distributions: pd.DataFrame,
property_restrictions: List[Dict[str, Any]] = None,
circumcision_reduced_acquire: float = 0.6,
distributed_event_trigger: str = 'Program_VMMC',
start_day: Union[int, float] = 1,
event_name: str = '',
node_ids: Optional[List[int]] = None
) -> None:
"""
Creates a new NChooserEventCoordinatorHIV event in the specified EMOD campaign with a MaleCircumcision intervention and
Distributions specified in distributions dataframe.
Args:
camp (emod_api.campaign): The campaign object to which the event will be added. This
should be an instance of the emod_api.campaign class.
target_disease_state (List[List[str]]): A 2D list specifying targeted disease states.
has_intervention_name_exclusion (str): The name of the intervention to look for in an individual when
targeting specific disease states. It's also the
Intervention_Name of the MaleCircumcision intervention.
distributions: A DataFrame contains the data for these columns: year, min_age,
max_age, n_circumcisions.
property_restrictions (List[Dict[str, Any]], optional): A list of property restrictions, each represented as
a dict with keys as property names and values as
property values. Deafult to None.
circumcision_reduced_acquire (Float, optional): Circumcision_Reduced_Acquire parameter: The reduction of
susceptibility to STI by voluntary male medical circumcision
(VMMC). Default to 0.6.
distributed_event_trigger(str, optional): This is the event that is broadcasted when the circumcision is
distributed to the man. One could add it to a report to say
count the number of men who were circumcised during the
reporting period. Default to 'Program_VMMC'.
start_day(int | float, optional): The day when the event starts. Defaults to 1.
event_name(str, optional): The name of the campaign event, default to empty string.
node_ids (list of int, optional): A list of node IDs where the event will be applied. If None,
the event applies to all nodes. Defaults to None.
Returns:
None: This function does not return anything. It modifies the campaign object in place.
Examples:
>>> import emod_api
>>> from emodpy_hiv.interventions import nchooser
>>> import pandas as pd
>>>
>>> campaign_obj = emod_api.campaign
>>> target_disease_state = [["HIV_Negative", "Not_Have_Intervention"]]
>>> has_intervention_name_exclusion = 'MaleCircumcision'
>>> data = {'year': [2010, 2010, 2011, 2011],
>>> 'min_age': [1, 15, 1, 15],
>>> 'max_age': [14.999, 49.999, 14.999, 49.999],
>>> 'n_circumcisions': [200, 1300, 290, 1490]}
>>> distributions = pd.DataFrame.from_dict(data)
>>> nchooser.add_nchooser_distributed_circumcision_event(campaign_obj, target_disease_state,
>>> has_intervention_name_exclusion, distributions,
>>> start_day=365, node_ids=[1, 2, 3])
"""
if not {'year', 'min_age', 'max_age', 'n_circumcisions'}.issubset(distributions.columns):
raise ValueError("Expected these columns: 'year', 'min_age', 'max_age', 'n_circumcisions' in the distributions "
f"dataframe. Got distributions.columns: {distributions.columns}")
target_distributions = []
# Get the unique value of target years from the distribution dataframe
target_years = distributions.year.unique()
# Created the distribution objects for each target year
for year in target_years:
# Filter the distribution dataframe with year
df_cur_year = distributions[distributions['year'] == year].sort_values(by=['min_age'])
# Max and Min age ranges for qualifying individuals.
age_ranges_years = [df_cur_year['max_age'].tolist(), df_cur_year['min_age'].tolist()]
# num_targeted_males is the values in n_circumcisions column,
# num_targeted_females contains 0s with the same length.
num_targeted_males = df_cur_year['n_circumcisions'].tolist()
num_targeted_females = [0] * len(num_targeted_males)
target_distribution = new_target_distribution(camp,
age_ranges_years=age_ranges_years,
start_year=year,
end_year=year + 0.999,
num_targeted_females=num_targeted_females,
num_targeted_males=num_targeted_males,
property_restrictions_within_node=property_restrictions,
target_disease_state=target_disease_state,
target_disease_state_has_intervention_name=
has_intervention_name_exclusion)
target_distributions.append(target_distribution)
# Create a MaleCircumcision intervention
intervention = malecirc.new_intervention(camp)
intervention.Circumcision_Reduced_Acquire = circumcision_reduced_acquire
intervention.Distributed_Event_Trigger = camp.get_send_trigger(distributed_event_trigger, True)
intervention.Intervention_Name = has_intervention_name_exclusion
# Call the add_nchooser_event function to add the MaleCircumcision with target_distributions
add_nchooser_event(camp, target_distributions=target_distributions, intervention_config=intervention,
start_day=start_day, event_name=event_name, node_ids=node_ids)