from emod_api import schema_to_class as s2c
[docs]def adherent_drug(campaign, cost: int = 1, doses: list = None, dose_interval: int = 1,
adherence_values: list = None,
non_adherence_options: list = None,
non_adherence_distribution: list = None, max_dose_consideration_duration: int = 40,
took_dose_event: str = "Took_Dose", intervention_name: str = None):
"""
Configures adherent drug dictionary using the **AdherentDrug** class, an individual-level
intervention which extends the **AntimalarialDrug** class.
Args:
campaign: campaign object to which the intervention will be added, and schema_path container
cost: Unit cost per drug.
doses: Lists of drugs for each dose. For example,
``[["DrugA", DrugB"], ["DrugB"], [], ["DrugB"]]``. The empty list,
``[]``, indicates no drugs for that dose.
dose_interval: Interval between doses of drugs, in days. Default is 1.
adherence_values: A list defining WaningEffectMapCount waning effect's "Values",
to be used to set the probability for a particular dose. Where the "Times"
is the dose number inferred from 'doses' parameter and "Values" is the probably
of that dose being successfully taken.
non_adherence_options: List of enums to define what happens when the user
is not adherent. If not defined then NEXT_UPDATE is used. Enum values are:
["STOP", "NEXT_UPDATE", "NEXT_DOSAGE_TIME", "LOST_TAKE_NEXT"].
non_adherence_distribution: Non adherence probability value(s) assigned to
the corresponding options in non_adherence_options. There must be one value
in this list for each value in non_adherence_options. The sum of these
values must equal 1.0.
max_dose_consideration_duration: Maximum number of days that an individual
will consider taking the doses of the drug.
took_dose_event: Event that gets sent out every time a dose is taken.
intervention_name: The optional name used to refer to this intervention as a means to differentiate it from
others that use the same class. Default is AdeherentDrug_drug1_drug2 in alphabetical order.
Returns:
Configured **AdherentDrug** class dictionary
"""
# built-in default so we can run this function by just putting in the config builder.
waning_map = s2c.get_class_with_defaults("WaningEffectMapCount", campaign.schema_path)
waning_map.Initial_Effect = 1
if not doses:
doses = [["Sulfadoxine", "Pyrimethamine", 'Amodiaquine'],
['Amodiaquine'],
['Amodiaquine']]
inferred_times = [x + 1 for x in range(len(doses))]
if not adherence_values:
adherence_values = [1, 1, 1]
# the default is for person to take everything every dose
waning_map.Durability_Map.Times = inferred_times
waning_map.Durability_Map.Values = adherence_values
if len(adherence_values) != len(inferred_times):
raise ValueError(f"Length of 'adherence_values' parameter ({len(adherence_values)}) does not match the "
f"length of 'doses' ({len(doses)}). These need to be the same as we are defining the "
f"probabilities of taking each dose.\n")
if not non_adherence_options:
non_adherence_options = ["NEXT_UPDATE"]
if not non_adherence_distribution:
non_adherence_distribution = [1]
adherent_drug = s2c.get_class_with_defaults("AdherentDrug", campaign.schema_path)
adherent_drug.Cost_To_Consumer = cost
adherent_drug.Doses = doses
adherent_drug.Dose_Interval = dose_interval
adherent_drug.Adherence_Config = waning_map
adherent_drug.Non_Adherence_Options = non_adherence_options
adherent_drug.Non_Adherence_Distribution = non_adherence_distribution
adherent_drug.Max_Dose_Consideration_Duration = max_dose_consideration_duration
adherent_drug.Took_Dose_Event = campaign.get_send_trigger(took_dose_event, old=True)
if not intervention_name:
all_drugs = []
for day in doses:
for drug in day:
all_drugs.append(drug)
all_drugs = sorted(list(set(all_drugs)))
intervention_name = "AdherentDrug"
for drug in all_drugs:
intervention_name = intervention_name + "_" + drug
adherent_drug.Intervention_Name = intervention_name
return adherent_drug