historical_vaccinate_prob#

class historical_vaccinate_prob(vaccine, days, label=None, prob=1.0, subtarget=None, compliance=1.0, **kwargs)[source]#

Bases: BaseVaccination

Probability-based historical vaccination

This vaccine intervention allocates vaccines parametrized by the daily probability of being vaccinated. Unlike cv.vaccinate_prob this function allows vaccination prior to t=0 (and continuing into the simulation).

If any people are infected at the t=0 timestep (e.g. seed infections), this finds those people and will re-infect them at the end of the historical vaccination. Thus you may have breakthrough infections and this might affect other interventions to initialize a population.

Parameters:

  • 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)

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

  • compliance (float/arr) – compliance of the person to take each dose (if scalar then applied per dose)

  • 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

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

See parameters.py for additional examples of these parameters.

Example:

pfizer = cv.historical_vaccinate_prob(vaccine='pfizer', days=np.arange(-30,0), prob=0.007) # 30-day vaccination campaign
cv.Sim(interventions=pfizer).run().plot()

New in version 3.1.0.

Methods

static process_days(sim, days, return_dates=False)[source]#

Ensure lists of days are in consistent format. Used by change_beta, clip_edges, and some analyzers. Optionally return dates as well as days. If days is callable, leave unchanged.

static estimate_prob(duration, coverage)[source]#

Estimate the per-day probability to achieve desired population coverage for a campaign of fixed duration and fixed per-day probability of a person being vaccinated

Parameters:

  • duration – length of campign in days

  • coverage – target coverage of campaign

Example:

prob = historical_vaccinate.estimate_prob(duration=180, coverage=0.70)