emodpy_malaria.interventions.adherentdrug module#

emodpy_malaria.interventions.adherentdrug.adherent_drug(campaign, cost: int = 1, doses: list | None = None, dose_interval: int = 1, adherence_values: list | None = None, non_adherence_options: list | None = None, non_adherence_distribution: list | None = None, max_dose_consideration_duration: int = 40, took_dose_event: str = 'Took_Dose', intervention_name: str | None = None)[source]#

Configures adherent drug dictionary using the AdherentDrug class, an individual-level intervention which extends the AntimalarialDrug class.

  • 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