make_scen

class make_scen(*args, **kwargs)

Alias for fp.Scenario().

Store the specification for a single scenario (which may consist of multiple interventions).

This function is intended to be as flexible as possible; as a result, it may be somewhat confusing. There are five different ways to call it – method efficacy, method probability, method initiation/discontinuation, parameter, and custom intervention.

Args (shared):

spec (dict): a pre-made specification of a scenario; see keyword explanations below (optional) args (list): additional specifications (optional) label (str): the sim label to use for this scenario pars (dict): optionally supply additional sim parameters to use with this scenario (that take effect at the beginning of the sim, not at the point of intervention) year (float): the year at which to activate efficacy and probability scenarios matrix (str): which set of probabilities to modify for probability scenarios (e.g. annual or postpartum) ages (str/list): the age groups to modify the probabilities for

Args (efficacy):

year (float): as above eff (dict): a dictionary of method names and new efficacy values

Args (probablity):

year (float): as above matrix (str): as above ages (str): as above source (str): the method to switch from dest (str): the method to switch to factor (float): if supplied, multiply the [source, dest] probability by this amount value (float): if supplied, instead of factor, replace the [source, dest] probability by this value copy_from (str): if supplied, copy probabilities from a different method

Args (initiation/discontinuation):

year (float): as above matrix (str): as above ages (str): as above method (str): the method for initiation/discontinuation init_factor (float): as with “factor” above, for initiation (None → method) discont_factor (float): as with “factor” above, for discontinuation (method → None) init_value (float): as with “value” above, for initiation (None → method) discont_value (float): as with “value” above, for discontinuation (method → None)

Args (parameter):

par (str): the parameter to modify par_years (float/list): the year(s) at which to apply the modifications par_vals (float/list): the value(s) of the parameter for each year

Args (custom):

interventions (Intervention/list): any custom intervention(s) to be applied to the scenario

Congratulations on making it this far.

Examples:

# Basic efficacy scenario
s1 = fp.make_scen(eff={'Injectables':0.99}, year=2020)

# Double rate of injectables initiation
s2 = fp.make_scen(source='None', dest='Injectables', factor=2)

# Double rate of injectables initiation -- alternate approach
s3 = fp.make_scen(method='Injectables', init_factor=2)

# More complex example: change condoms to injectables transition probability for 18-25 postpartum women
s4 = fp.make_scen(source='Condoms', dest='Injectables', value=0.5, ages='18-25', matrix='pp1to6')

# Parameter scenario: halve exposure
s5 = fp.make_scen(par='exposure_factor', years=2010, vals=0.5)

# Custom scenario
def update_sim(sim): sim.updated = True
s6 = fp.make_scen(interventions=update_sim)

# Combining multiple scenarios: change probabilities and exposure factor
s7 = fp.make_scen(
    dict(method='Injectables', init_value=0.1, discont_value=0.02, create=True),
    dict(par='exposure_factor', years=2010, vals=0.5)
)

# Scenarios can be combined
s8 = s1 + s2