Source code for hpvsim.immunity

'''
Defines classes and methods for calculating immunity
'''

import numpy as np
import sciris as sc
from collections.abc import Iterable
from . import utils as hpu
from . import defaults as hpd
from . import parameters as hppar
from . import interventions as hpi


# %% Immunity methods

[docs]def init_immunity(sim, create=True): ''' Initialize immunity matrices with all genotypes and vaccines in the sim''' # Pull out all of the circulating genotypes for cross-immunity ng = sim['n_genotypes'] # Pull out all the vaccination interventions vx_intvs = [x for x in sim['interventions'] if isinstance(x, hpi.BaseVaccination)] nv = len(vx_intvs) # Dimension for immunity matrix ndim = ng + nv # If immunity values have been provided, process them if sim['immunity'] is None or create: # Precompute waning - same for all genotypes if sim['use_waning']: imm_decay = sc.dcp(sim['imm_decay']) if 'half_life' in imm_decay.keys(): imm_decay['half_life'] /= sim['dt'] sim['imm_kin'] = precompute_waning(t=sim.tvec, pars=imm_decay) sim['immunity_map'] = dict() # Firstly, initialize immunity matrix with defaults. These are then overwitten with specific values below immunity = np.ones((ng, ng), dtype=hpd.default_float) # Fill with defaults # Next, overwrite these defaults with any known immunity values about specific genotypes default_cross_immunity = hppar.get_cross_immunity(cross_imm_med=sim['cross_imm_med'], cross_imm_high=sim['cross_imm_high']) for i in range(ng): sim['immunity_map'][i] = 'infection' label_i = sim['genotype_map'][i] for j in range(ng): label_j = sim['genotype_map'][j] if label_i in default_cross_immunity and label_j in default_cross_immunity: immunity[j][i] = default_cross_immunity[label_j][label_i] imm_source = ng for vi,vx_intv in enumerate(vx_intvs): genotype_pars_df = vx_intv.product.genotype_pars[vx_intv.product.genotype_pars.genotype.isin(sim['genotype_map'].values())] # TODO fix this vacc_mapping = [genotype_pars_df[genotype_pars_df.genotype==gtype].rel_imm.values[0] for gtype in sim['genotype_map'].values()] vacc_mapping += [1]*(vi+1) # Add on some ones to pad out the matrix vacc_mapping = np.reshape(vacc_mapping, (len(immunity)+1, 1)).astype(hpd.default_float) # Reshape immunity = np.hstack((immunity, vacc_mapping[0:len(immunity),])) immunity = np.vstack((immunity, np.transpose(vacc_mapping))) vx_intv.product.imm_source = imm_source imm_source += 1 sim['immunity'] = immunity sim['immunity'] = sim['immunity'].astype('float32') sim['n_imm_sources'] = ndim return
[docs]def 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 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). Args: 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 ''' if infection: # Determine whether individual seroconverts based upon genotype genotype_label = imm_pars['genotype_map'][imm_source] genotype_pars = imm_pars['genotype_pars'][genotype_label] seroconvert_probs = np.full(len(inds), fill_value=genotype_pars.sero_prob) is_seroconvert = hpu.binomial_arr(seroconvert_probs) # Extract parameters and indices has_imm = people.nab_imm[imm_source, inds] > 0 no_prior_imm_inds = inds[~has_imm] prior_imm_inds = inds[has_imm] if len(prior_imm_inds): new_peak = hpu.sample(**imm_pars['imm_init'], size=len(prior_imm_inds)) people.peak_imm[imm_source, prior_imm_inds] = np.maximum(new_peak, people.peak_imm[imm_source, prior_imm_inds]) new_cell_imm = hpu.sample(**imm_pars['cell_imm_init'], size=len(prior_imm_inds)) people.cell_imm[imm_source, prior_imm_inds] = np.maximum(new_cell_imm, people.cell_imm[imm_source, prior_imm_inds]) if len(no_prior_imm_inds): people.peak_imm[imm_source, no_prior_imm_inds] = is_seroconvert[~has_imm] * hpu.sample(**imm_pars['imm_init'], size=len(no_prior_imm_inds)) people.cell_imm[imm_source, no_prior_imm_inds] = is_seroconvert[~has_imm] * hpu.sample(**imm_pars['cell_imm_init'], size=len(no_prior_imm_inds)) else: # Vaccination by dose dose1_inds = inds[people.doses[inds]==1] # First doses dose2_inds = inds[people.doses[inds]==2] # Second doses dose3_inds = inds[people.doses[inds]==3] # Third doses if imm_pars['doses']>1: imm_pars['imm_boost'] = sc.promotetolist(imm_pars['imm_boost']) if len(dose1_inds)>0: # Initialize immunity for newly vaccinated people people.peak_imm[imm_source, dose1_inds] = hpu.sample(**imm_pars['imm_init'], size=len(dose1_inds)) if len(dose2_inds) > 0: # Boost immunity for people receiving 2nd dose... people.peak_imm[imm_source, dose2_inds] *= imm_pars['imm_boost'][0] if len(dose3_inds) > 0: people.peak_imm[imm_source, dose3_inds] *= imm_pars['imm_boost'][1] base_t = people.t + offset if offset is not None else people.t people.t_imm_event[imm_source, inds] = base_t return
[docs]def 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. ''' immunity = people.pars['immunity'] # cross-immunity/own-immunity scalars to be applied to immunity level sus_imm = np.dot(immunity,people.nab_imm) # Dot product gives immunity to all genotypes people.sus_imm[:] = np.minimum(sus_imm, np.ones_like(sus_imm)) # Don't let this be above 1 return
#%% Methods for computing waning
[docs]def 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. Args: 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 ''' pars = sc.dcp(pars) form = pars.pop('form') choices = [ 'exp_decay', ] # Process inputs if form is None: output = np.ones(len(t), dtype=hpd.default_float) elif form == 'exp_decay': if pars['half_life'] is None: pars['half_life'] = np.nan output = exp_decay(t, **pars) elif callable(form): output = form(t, **pars) else: errormsg = f'The selected functional form "{form}" is not implemented; choices are: {sc.strjoin(choices)}' raise NotImplementedError(errormsg) return output
[docs]def exp_decay(t, init_val, half_life): ''' Returns an array of length t with values for the immunity at each time step after recovery ''' decay_rate = np.log(2) / half_life if ~np.isnan(half_life) else 0. result = init_val * np.exp(-decay_rate * t, dtype=hpd.default_float) return result
[docs]def linear_decay(length, init_val, slope): ''' Calculate linear decay ''' result = -slope*np.ones(length) result[0] = init_val return result
[docs]def linear_growth(length, slope): ''' Calculate linear growth ''' return slope*np.ones(length)