class vaccinate_prob(vaccine, days, label=None, prob=None, subtarget=None, booster=False, **kwargs)[source]#

Bases: BaseVaccination

Probability-based vaccination

This vaccine intervention allocates vaccines parametrized by the daily probability of being vaccinated.

  • vaccine (dict/str) – which vaccine to use; see below for dict parameters

  • label (str) – if vaccine is supplied as a dict, the name of the vaccine

  • days (int/arr) – the day or array of days to apply the interventions

  • prob (float) – probability of being vaccinated (i.e., fraction of the population)

  • booster (bool) – whether it’s a booster (i.e. targeted to vaccinated people) or not

  • subtarget (dict) – subtarget intervention to people with particular indices (see test_num() for details)

  • kwargs (dict) – passed to Intervention()

If vaccine is supplied as a dictionary, it must have the following parameters:

  • nab_eff: the waning efficacy of neutralizing antibodies at preventing infection

  • nab_init: the initial antibody level (higher = more protection)

  • nab_boost: how much of a boost being vaccinated on top of a previous dose or natural infection provides

  • doses: the number of doses required to be fully vaccinated

  • interval: the interval between doses (integer)

  • entries for efficacy against each of the strains (e.g. b117)

See for additional examples of these parameters.


pfizer = cv.vaccinate_prob(vaccine='pfizer', days=30, prob=0.7)
cv.Sim(interventions=pfizer, use_waning=True).run().plot()