immunity

immunity

Defines classes and methods for calculating immunity

Functions

Name	Description
check_immunity	Calculate people’s immunity on this timestep from prior infections.
exp_decay	Returns an array of length t with values for the immunity at each time step after recovery
init_immunity	Initialize immunity matrices with all genotypes and vaccines in the sim
linear_decay	Calculate linear decay
linear_growth	Calculate linear growth
precompute_waning	Process functional form and parameters into values:
update_peak_immunity	Update immunity level

check_immunity

immunity.check_immunity(people)

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.

exp_decay

immunity.exp_decay(t, init_val, half_life)

Returns an array of length t with values for the immunity at each time step after recovery

init_immunity

immunity.init_immunity(sim, create=True)

Initialize immunity matrices with all genotypes and vaccines in the sim

linear_decay

immunity.linear_decay(length, init_val, slope)

Calculate linear decay

linear_growth

immunity.linear_growth(length, slope)

Calculate linear growth

precompute_waning

immunity.precompute_waning(t, pars=None)

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

Name	Type	Description	Default
length	float	length of array to return, i.e., for how long waning is calculated	required
pars	dict	passed to individual immunity functions	None

Returns

Name	Type	Description
		array of length ‘length’ of values

update_peak_immunity

immunity.update_peak_immunity(
    people,
    inds,
    imm_pars,
    imm_source,
    offset=None,
    infection=True,
)

Update immunity level

This function updates the immunity for individuals when an infection is cleared 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

Name	Type	Description	Default
people		A people object	required
inds		Array of people indices	required
imm_pars		Parameters from which to draw values for quantities like [‘imm_init’] - either sim pars (for natural immunity) or vaccine pars	required
imm_source		index of either genotype or vaccine where immunity is coming from	required

Returns: None