hpvsim.immunity module¶
Defines classes and methods for calculating immunity
- init_immunity(sim, create=True)[source]¶
Initialize immunity matrices with all genotypes and vaccines in the sim
- update_peak_immunity(people, inds, imm_pars, imm_source, offset=None, infection=True)[source]¶
Update immunity level
- This function updates the immunity for individuals when an infection or vaccination occurs.
individuals that are infected and already have immunity from a previous vaccination/infection have their immunity level;
- individuals without prior immunity are assigned an initial level drawn from a distribution. This level
depends on whether the immunity is from a natural infection or from a vaccination (and if so, on the type of vaccine).
- Parameters:
people – A people object
inds – Array of people indices
imm_pars – Parameters from which to draw values for quantities like [‘imm_init’] - either sim pars (for natural immunity) or vaccine pars
imm_source – index of either genotype or vaccine where immunity is coming from
Returns: None
- check_immunity(people)[source]¶
Calculate people’s immunity on this timestep from prior infections. As an example, suppose HPV16 and 18 are in the sim, and the cross-immunity matrix is:
- pars[‘immunity’] = np.array([[1., 0.5],
[0.3, 1.]])
This indicates that people who’ve had HPV18 have 50% protection against getting 16, and people who’ve had 16 have 30% protection against getting 18. Now suppose we have 3 people, whose immunity levels are
- people.nab_imm = np.array([[0.9, 0.0, 0.0],
[0.0, 0.7, 0.0]])
This indicates that person 1 has a prior HPV16 infection, person 2 has a prior HPV18 infection, and person 3 has no history of infection.
In this function, we take the dot product of pars[‘immunity’] and people.nab_imm to get:
- people.sus_imm = np.array([[0.9 , 0.35, 0. ],
[0.27, 0.7 , 0. ]])
This indicates that the person 1 has high protection against reinfection with HPV16, and some (30% of 90%) protection against infection with HPV18, and so on.
- precompute_waning(t, pars=None)[source]¶
Process functional form and parameters into values:
‘exp_decay’ : exponential decay. Parameters should be init_val and half_life (half_life can be None/nan)
‘linear_decay’: linear decay
A custom function can also be supplied.
- Parameters:
length (float) – length of array to return, i.e., for how long waning is calculated
pars (dict) – passed to individual immunity functions
- Returns:
array of length ‘length’ of values