Source code for covasim.parameters

'''
Set the parameters for Covasim.
'''

import numpy as np
import sciris as sc
from .settings import options as cvo # For setting global options
from . import misc as cvm
from . import defaults as cvd

__all__ = ['make_pars', 'reset_layer_pars', 'get_prognoses', 'get_variant_choices', 'get_vaccine_choices',
           'get_variant_pars', 'get_cross_immunity', 'get_vaccine_variant_pars', 'get_vaccine_dose_pars']


[docs]def make_pars(set_prognoses=False, prog_by_age=True, version=None, **kwargs): ''' Create the parameters for the simulation. Typically, this function is used internally rather than called by the user; e.g. typical use would be to do sim = cv.Sim() and then inspect sim.pars, rather than calling this function directly. Args: set_prognoses (bool): whether or not to create prognoses (else, added when the population is created) prog_by_age (bool): whether or not to use age-based severity, mortality etc. kwargs (dict): any additional kwargs are interpreted as parameter names version (str): if supplied, use parameters from this Covasim version Returns: pars (dict): the parameters of the simulation ''' pars = {} # Population parameters pars['pop_size'] = 20e3 # Number of agents, i.e., people susceptible to SARS-CoV-2 pars['pop_infected'] = 20 # Number of initial infections pars['pop_type'] = 'random' # What type of population data to use -- 'random' (fastest), 'synthpops' (best), 'hybrid' (compromise) pars['location'] = None # What location to load data from -- default Seattle # Simulation parameters pars['start_day'] = '2020-03-01' # Start day of the simulation pars['end_day'] = None # End day of the simulation pars['n_days'] = 60 # Number of days to run, if end_day isn't specified pars['rand_seed'] = 1 # Random seed, if None, don't reset pars['verbose'] = cvo.verbose # Whether or not to display information during the run -- options are 0 (silent), 0.1 (some; default), 1 (default), 2 (everything) # Rescaling parameters pars['pop_scale'] = 1 # Factor by which to scale the population -- e.g. pop_scale=10 with pop_size=100e3 means a population of 1 million pars['scaled_pop'] = None # The total scaled population, i.e. the number of agents times the scale factor pars['rescale'] = True # Enable dynamic rescaling of the population -- starts with pop_scale=1 and scales up dynamically as the epidemic grows pars['rescale_threshold'] = 0.05 # Fraction susceptible population that will trigger rescaling if rescaling pars['rescale_factor'] = 1.2 # Factor by which the population is rescaled on each step pars['frac_susceptible'] = 1.0 # What proportion of the population is susceptible to infection # Network parameters, generally initialized after the population has been constructed pars['contacts'] = None # The number of contacts per layer; set by reset_layer_pars() below pars['dynam_layer'] = None # Which layers are dynamic; set by reset_layer_pars() below pars['beta_layer'] = None # Transmissibility per layer; set by reset_layer_pars() below # Basic disease transmission parameters pars['beta_dist'] = dict(dist='neg_binomial', par1=1.0, par2=0.45, step=0.01) # Distribution to draw individual level transmissibility; dispersion from https://www.researchsquare.com/article/rs-29548/v1 pars['viral_dist'] = dict(frac_time=0.3, load_ratio=2, high_cap=4) # The time varying viral load (transmissibility); estimated from Lescure 2020, Lancet, https://doi.org/10.1016/S1473-3099(20)30200-0 pars['beta'] = 0.016 # Beta per symptomatic contact; absolute value, calibrated pars['asymp_factor'] = 1.0 # Multiply beta by this factor for asymptomatic cases; no statistically significant difference in transmissibility: https://www.sciencedirect.com/science/article/pii/S1201971220302502 # Parameters that control settings and defaults for multi-variant runs pars['n_imports'] = 0 # Average daily number of imported cases (actual number is drawn from Poisson distribution) pars['n_variants'] = 1 # The number of variants circulating in the population # Parameters used to calculate immunity pars['use_waning'] = True # Whether to use dynamically calculated immunity pars['nab_init'] = dict(dist='normal', par1=0, par2=2) # Parameters for the distribution of the initial level of log2(nab) following natural infection, taken from fig1b of https://doi.org/10.1101/2021.03.09.21252641 pars['nab_decay'] = dict(form='nab_growth_decay', growth_time=21, decay_rate1=np.log(2) / 50, decay_time1=150, decay_rate2=np.log(2) / 250, decay_time2=365) pars['nab_kin'] = None # Constructed during sim initialization using the nab_decay parameters pars['nab_boost'] = 1.5 # Multiplicative factor applied to a person's nab levels if they get reinfected. No data on this, assumption. pars['nab_eff'] = dict(alpha_inf=1.08, alpha_inf_diff=1.812, beta_inf=0.967, alpha_symp_inf=-0.739, beta_symp_inf=0.038, alpha_sev_symp=-0.014, beta_sev_symp=0.079) # Parameters to map nabs to efficacy pars['rel_imm_symp'] = dict(asymp=0.85, mild=1, severe=1.5) # Relative immunity from natural infection varies by symptoms. Assumption. pars['immunity'] = None # Matrix of immunity and cross-immunity factors, set by init_immunity() in immunity.py pars['trans_redux'] = 0.59 # Reduction in transmission for breakthrough infections, https://www.medrxiv.org/content/10.1101/2021.07.13.21260393v # Variant-specific disease transmission parameters. By default, these are set up for a single variant, but can all be modified for multiple variants pars['rel_beta'] = 1.0 # Relative transmissibility varies by variant # Duration parameters: time for disease progression pars['dur'] = {} pars['dur']['exp2inf'] = dict(dist='lognormal_int', par1=4.5, par2=1.5) # Duration from exposed to infectious; see Lauer et al., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081172/, appendix table S2, subtracting inf2sym duration pars['dur']['inf2sym'] = dict(dist='lognormal_int', par1=1.1, par2=0.9) # Duration from infectious to symptomatic; see Linton et al., https://doi.org/10.3390/jcm9020538, from Table 2, 5.6 day incubation period - 4.5 day exp2inf from Lauer et al. pars['dur']['sym2sev'] = dict(dist='lognormal_int', par1=6.6, par2=4.9) # Duration from symptomatic to severe symptoms; see Linton et al., https://doi.org/10.3390/jcm9020538, from Table 2, 6.6 day onset to hospital admission (deceased); see also Wang et al., https://jamanetwork.com/journals/jama/fullarticle/2761044, 7 days (Table 1) pars['dur']['sev2crit'] = dict(dist='lognormal_int', par1=1.5, par2=2.0) # Duration from severe symptoms to requiring ICU; average of 1.9 and 1.0; see Chen et al., https://www.sciencedirect.com/science/article/pii/S0163445320301195, 8.5 days total - 6.6 days sym2sev = 1.9 days; see also Wang et al., https://jamanetwork.com/journals/jama/fullarticle/2761044, Table 3, 1 day, IQR 0-3 days; std=2.0 is an estimate # Duration parameters: time for disease recovery pars['dur']['asym2rec'] = dict(dist='lognormal_int', par1=8.0, par2=2.0) # Duration for asymptomatic people to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x pars['dur']['mild2rec'] = dict(dist='lognormal_int', par1=8.0, par2=2.0) # Duration for people with mild symptoms to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x pars['dur']['sev2rec'] = dict(dist='lognormal_int', par1=18.1, par2=6.3) # Duration for people with severe symptoms to recover, 24.7 days total; see Verity et al., https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext; 18.1 days = 24.7 onset-to-recovery - 6.6 sym2sev; 6.3 = 0.35 coefficient of variation * 18.1; see also https://doi.org/10.1017/S0950268820001259 (22 days) and https://doi.org/10.3390/ijerph17207560 (3-10 days) pars['dur']['crit2rec'] = dict(dist='lognormal_int', par1=18.1, par2=6.3) # Duration for people with critical symptoms to recover; as above (Verity et al.) pars['dur']['crit2die'] = dict(dist='lognormal_int', par1=10.7, par2=4.8) # Duration from critical symptoms to death, 18.8 days total; see Verity et al., https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext; 10.7 = 18.8 onset-to-death - 6.6 sym2sev - 1.5 sev2crit; 4.8 = 0.45 coefficient of variation * 10.7 # Severity parameters: probabilities of symptom progression pars['rel_symp_prob'] = 1.0 # Scale factor for proportion of symptomatic cases pars['rel_severe_prob'] = 1.0 # Scale factor for proportion of symptomatic cases that become severe pars['rel_crit_prob'] = 1.0 # Scale factor for proportion of severe cases that become critical pars['rel_death_prob'] = 1.0 # Scale factor for proportion of critical cases that result in death pars['prog_by_age'] = prog_by_age # Whether to set disease progression based on the person's age pars['prognoses'] = None # The actual arrays of prognoses by age; this is populated later # Efficacy of protection measures pars['iso_factor'] = None # Multiply beta by this factor for diagnosed cases to represent isolation; set by reset_layer_pars() below pars['quar_factor'] = None # Quarantine multiplier on transmissibility and susceptibility; set by reset_layer_pars() below pars['quar_period'] = 14 # Number of days to quarantine for; assumption based on standard policies # Events and interventions pars['interventions'] = [] # The interventions present in this simulation; populated by the user pars['analyzers'] = [] # Custom analysis functions; populated by the user pars['timelimit'] = None # Time limit for the simulation (seconds) pars['stopping_func'] = None # A function to call to stop the sim partway through # Health system parameters pars['n_beds_hosp'] = None # The number of hospital (adult acute care) beds available for severely ill patients (default is no constraint) pars['n_beds_icu'] = None # The number of ICU beds available for critically ill patients (default is no constraint) pars['no_hosp_factor'] = 2.0 # Multiplier for how much more likely severely ill people are to become critical if no hospital beds are available pars['no_icu_factor'] = 2.0 # Multiplier for how much more likely critically ill people are to die if no ICU beds are available # Handle vaccine and variant parameters pars['vaccine_pars'] = {} # Vaccines that are being used; populated during initialization pars['vaccine_map'] = {} #Reverse mapping from number to vaccine key pars['variants'] = [] # Additional variants of the virus; populated by the user, see immunity.py pars['variant_map'] = {0:'wild'} # Reverse mapping from number to variant key pars['variant_pars'] = dict(wild={}) # Populated just below for sp in cvd.variant_pars: if sp in pars.keys(): pars['variant_pars']['wild'][sp] = pars[sp] # Update with any supplied parameter values and generate things that need to be generated pars.update(kwargs) reset_layer_pars(pars) if set_prognoses: # If not set here, gets set when the population is initialized pars['prognoses'] = get_prognoses(pars['prog_by_age'], version=version) # Default to age-specific prognoses # If version is specified, load old parameters if version is not None: version_pars = cvm.get_version_pars(version, verbose=pars['verbose']) if sc.compareversions(version, '<3.0.0'): # Waning was introduced in 3.0, so is always false before version_pars['use_waning'] = False for key in pars.keys(): # Only loop over keys that have been populated if key in version_pars: # Only replace keys that exist in the old version pars[key] = version_pars[key] # Handle code change migration if sc.compareversions(version, '<2.1.0') and 'migrate_lognormal' not in pars: cvm.migrate_lognormal(pars, verbose=pars['verbose']) return pars
# Define which parameters need to be specified as a dictionary by layer -- define here so it's available at the module level for sim.py layer_pars = ['beta_layer', 'contacts', 'dynam_layer', 'iso_factor', 'quar_factor']
[docs]def reset_layer_pars(pars, layer_keys=None, force=False): ''' Helper function to set layer-specific parameters. If layer keys are not provided, then set them based on the population type. This function is not usually called directly by the user, although it can sometimes be used to fix layer key mismatches (i.e. if the contact layers in the population do not match the parameters). More commonly, however, mismatches need to be fixed explicitly. Args: pars (dict): the parameters dictionary layer_keys (list): the layer keys of the population, if available force (bool): reset the parameters even if they already exist ''' # Specify defaults for random -- layer 'a' for 'all' layer_defaults = {} layer_defaults['random'] = dict( beta_layer = dict(a=1.0), # Default beta contacts = dict(a=20), # Default number of contacts dynam_layer = dict(a=0), # Do not use dynamic layers by default iso_factor = dict(a=0.2), # Assumed isolation factor quar_factor = dict(a=0.3), # Assumed quarantine factor ) # Specify defaults for hybrid -- household, school, work, and community layers (h, s, w, c) layer_defaults['hybrid'] = dict( beta_layer = dict(h=3.0, s=0.6, w=0.6, c=0.3), # Per-population beta weights; relative; in part based on Table S14 of https://science.sciencemag.org/content/sci/suppl/2020/04/28/science.abb8001.DC1/abb8001_Zhang_SM.pdf contacts = dict(h=2.0, s=20, w=16, c=20), # Number of contacts per person per day, estimated dynam_layer = dict(h=0, s=0, w=0, c=0), # Which layers are dynamic -- none by default iso_factor = dict(h=0.3, s=0.1, w=0.1, c=0.1), # Multiply beta by this factor for people in isolation quar_factor = dict(h=0.6, s=0.2, w=0.2, c=0.2), # Multiply beta by this factor for people in quarantine ) # Specify defaults for SynthPops -- same as hybrid except for LTCF layer (l) l_pars = dict(beta_layer=1.5, contacts=10, dynam_layer=0, iso_factor=0.2, quar_factor=0.3) layer_defaults['synthpops'] = sc.dcp(layer_defaults['hybrid']) for key,val in l_pars.items(): layer_defaults['synthpops'][key]['l'] = val # Choose the parameter defaults based on the population type, and get the layer keys try: defaults = layer_defaults[pars['pop_type']] except Exception as E: errormsg = f'Cannot load defaults for population type "{pars["pop_type"]}": must be hybrid, random, or synthpops' raise ValueError(errormsg) from E default_layer_keys = list(defaults['beta_layer'].keys()) # All layers should be the same, but use beta_layer for convenience # Actually set the parameters for pkey in layer_pars: par = {} # Initialize this parameter default_val = layer_defaults['random'][pkey]['a'] # Get the default value for this parameter # If forcing, we overwrite any existing parameter values if force: par_dict = defaults[pkey] # Just use defaults else: par_dict = sc.mergedicts(defaults[pkey], pars.get(pkey, None)) # Use user-supplied parameters if available, else default # Figure out what the layer keys for this parameter are (may be different between parameters) if layer_keys: par_layer_keys = layer_keys # Use supplied layer keys else: par_layer_keys = list(sc.odict.fromkeys(default_layer_keys + list(par_dict.keys()))) # If not supplied, use the defaults, plus any extra from the par_dict; adapted from https://www.askpython.com/python/remove-duplicate-elements-from-list-python # Construct this parameter, layer by layer for lkey in par_layer_keys: # Loop over layers par[lkey] = par_dict.get(lkey, default_val) # Get the value for this layer if available, else use the default for random pars[pkey] = par # Save this parameter to the dictionary return
[docs]def get_prognoses(by_age=True, version=None): ''' Return the default parameter values for prognoses The prognosis probabilities are conditional given the previous disease state. Args: by_age (bool): whether to use age-specific values (default true) Returns: prog_pars (dict): the dictionary of prognosis probabilities ''' if not by_age: # All rough estimates -- almost always, prognoses by age (below) are used instead prognoses = dict( age_cutoffs = np.array([0]), sus_ORs = np.array([1.00]), trans_ORs = np.array([1.00]), symp_probs = np.array([0.75]), comorbidities = np.array([1.00]), severe_probs = np.array([0.10]), crit_probs = np.array([0.04]), death_probs = np.array([0.01]), ) else: prognoses = dict( age_cutoffs = np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90,]), # Age cutoffs (lower limits) sus_ORs = np.array([0.34, 0.67, 1.00, 1.00, 1.00, 1.00, 1.24, 1.47, 1.47, 1.47]), # Odds ratios for relative susceptibility -- from Zhang et al., https://science.sciencemag.org/content/early/2020/05/04/science.abb8001; 10-20 and 60-70 bins are the average across the ORs trans_ORs = np.array([1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]), # Odds ratios for relative transmissibility -- no evidence of differences comorbidities = np.array([1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]), # Comorbidities by age -- set to 1 by default since already included in disease progression rates symp_probs = np.array([0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.90]), # Overall probability of developing symptoms (based on https://www.medrxiv.org/content/10.1101/2020.03.24.20043018v1.full.pdf, scaled for overall symptomaticity) severe_probs = np.array([0.00050, 0.00165, 0.00720, 0.02080, 0.03430, 0.07650, 0.13280, 0.20655, 0.24570, 0.24570]), # Overall probability of developing severe symptoms (derived from Table 1 of https://www.imperial.ac.uk/media/imperial-college/medicine/mrc-gida/2020-03-16-COVID19-Report-9.pdf) crit_probs = np.array([0.00003, 0.00008, 0.00036, 0.00104, 0.00216, 0.00933, 0.03639, 0.08923, 0.17420, 0.17420]), # Overall probability of developing critical symptoms (derived from Table 1 of https://www.imperial.ac.uk/media/imperial-college/medicine/mrc-gida/2020-03-16-COVID19-Report-9.pdf) death_probs = np.array([0.00002, 0.00002, 0.00010, 0.00032, 0.00098, 0.00265, 0.00766, 0.02439, 0.08292, 0.16190]), # Overall probability of dying -- from O'Driscoll et al., https://www.nature.com/articles/s41586-020-2918-0; last data point from Brazeau et al., https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-34-ifr/ ) prognoses = relative_prognoses(prognoses) # Convert to conditional probabilities # If version is specified, load old parameters if by_age and version is not None: version_prognoses = cvm.get_version_pars(version, verbose=False)['prognoses'] for key in version_prognoses.keys(): # Only loop over keys that have been populated if key in version_prognoses: # Only replace keys that exist in the old version prognoses[key] = np.array(version_prognoses[key]) # Check that lengths match expected_len = len(prognoses['age_cutoffs']) for key,val in prognoses.items(): this_len = len(prognoses[key]) if this_len != expected_len: # pragma: no cover errormsg = f'Lengths mismatch in prognoses: {expected_len} age bins specified, but key "{key}" has {this_len} entries' raise ValueError(errormsg) return prognoses
def relative_prognoses(prognoses): ''' Convenience function to revert absolute prognoses into relative (conditional) ones. Internally, Covasim uses relative prognoses. ''' out = sc.dcp(prognoses) out['death_probs'] /= out['crit_probs'] # Conditional probability of dying, given critical symptoms out['crit_probs'] /= out['severe_probs'] # Conditional probability of symptoms becoming critical, given severe out['severe_probs'] /= out['symp_probs'] # Conditional probability of symptoms becoming severe, given symptomatic return out def absolute_prognoses(prognoses): ''' Convenience function to revert relative (conditional) prognoses into absolute ones. Used to convert internally used relative prognoses into more readable absolute ones. **Example**:: sim = cv.Sim() abs_progs = cv.parameters.absolute_prognoses(sim['prognoses']) ''' out = sc.dcp(prognoses) out['severe_probs'] *= out['symp_probs'] # Absolute probability of severe symptoms out['crit_probs'] *= out['severe_probs'] # Absolute probability of critical symptoms out['death_probs'] *= out['crit_probs'] # Absolute probability of dying return out #%% Variant, vaccine, and immunity parameters and functions
[docs]def get_variant_choices(): ''' Define valid pre-defined variant names ''' # List of choices currently available: new ones can be added to the list along with their aliases choices = { 'wild': ['wild', 'default', 'pre-existing', 'original'], 'alpha': ['alpha', 'b117', 'uk', 'united kingdom', 'kent'], 'beta': ['beta', 'b1351', 'sa', 'south africa'], 'gamma': ['gamma', 'p1', 'b11248', 'brazil'], 'delta': ['delta', 'b16172', 'india'], } mapping = {name:key for key,synonyms in choices.items() for name in synonyms} # Flip from key:value to value:key return choices, mapping
[docs]def get_vaccine_choices(): ''' Define valid pre-defined vaccine names ''' # List of choices currently available: new ones can be added to the list along with their aliases choices = { 'default': ['default', None], 'pfizer': ['pfizer', 'biontech', 'pfizer-biontech', 'pf', 'pfz', 'pz', 'bnt162b2', 'comirnaty'], 'moderna': ['moderna', 'md', 'spikevax'], 'novavax': ['novavax', 'nova', 'covovax', 'nvx', 'nv'], 'az': ['astrazeneca', 'az', 'covishield', 'oxford', 'vaxzevria'], 'jj': ['jnj', 'johnson & johnson', 'janssen', 'jj'], 'sinovac': ['sinovac', 'coronavac'], 'sinopharm': ['sinopharm'] } mapping = {name:key for key,synonyms in choices.items() for name in synonyms} # Flip from key:value to value:key return choices, mapping
def _get_from_pars(pars, default=False, key=None, defaultkey='default'): ''' Helper function to get the right output from vaccine and variant functions ''' # If a string was provided, interpret it as a key and swap if isinstance(default, str): key, default = default, key # Handle output if key is not None: try: return pars[key] except Exception as E: errormsg = f'Key "{key}" not found; choices are: {sc.strjoin(pars.keys())}' raise sc.KeyNotFoundError(errormsg) from E elif default: return pars[defaultkey] else: return pars
[docs]def get_variant_pars(default=False, variant=None): ''' Define the default parameters for the different variants ''' pars = dict( wild = dict( rel_beta = 1.0, # Default values rel_symp_prob = 1.0, # Default values rel_severe_prob = 1.0, # Default values rel_crit_prob = 1.0, # Default values rel_death_prob = 1.0, # Default values ), alpha = dict( rel_beta = 1.67, # Midpoint of the range reported in https://science.sciencemag.org/content/372/6538/eabg3055 rel_symp_prob = 1.0, # Inconclusive evidence on the likelihood of symptom development. See https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(21)00055-4/fulltext rel_severe_prob = 1.64, # From https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3792894, and consistent with https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.16.2100348 and https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/961042/S1095_NERVTAG_update_note_on_B.1.1.7_severity_20210211.pdf rel_crit_prob = 1.0, # Various studies have found increased mortality for B117 (summary here: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(21)00201-2/fulltext#tbl1), but not necessarily when conditioned on having developed severe disease rel_death_prob = 1.0, # See comment above ), beta = dict( rel_beta = 1.0, # No increase in transmissibility; B1351's fitness advantage comes from the reduction in neutralisation rel_symp_prob = 1.0, rel_severe_prob = 3.6, # From https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.16.2100348 rel_crit_prob = 1.0, rel_death_prob = 1.0, ), gamma = dict( rel_beta = 2.05, # Estimated to be 1.7–2.4-fold more transmissible than wild-type: https://science.sciencemag.org/content/early/2021/04/13/science.abh2644 rel_symp_prob = 1.0, rel_severe_prob = 2.6, # From https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.16.2100348 rel_crit_prob = 1.0, rel_death_prob = 1.0, ), delta = dict( rel_beta = 2.2, # Estimated to be 1.25-1.6-fold more transmissible than B117: https://www.researchsquare.com/article/rs-637724/v1 rel_symp_prob = 1.0, rel_severe_prob = 3.2, # 2x more transmissible than alpha from https://mobile.twitter.com/dgurdasani1/status/1403293582279294983 rel_crit_prob = 1.0, rel_death_prob = 1.0, ) ) return _get_from_pars(pars, default, key=variant, defaultkey='wild')
[docs]def get_cross_immunity(default=False, variant=None): ''' Get the cross immunity between each variant in a sim ''' pars = dict( wild = dict( wild = 1.0, # Default for own-immunity alpha = 0.5, # Assumption beta = 0.5, # https://www.nature.com/articles/s41586-021-03471-w gamma = 0.34, # Assumption delta = 0.374, # Assumption ), alpha = dict( wild = 0.5, # Assumption alpha = 1.0, # Default for own-immunity beta = 0.8, # Assumption gamma = 0.8, # Assumption delta = 0.689 # Assumption ), beta = dict( wild = 0.066, # https://www.nature.com/articles/s41586-021-03471-w alpha = 0.5, # Assumption beta = 1.0, # Default for own-immunity gamma = 0.5, # Assumption delta = 0.086 # Assumption ), gamma = dict( wild = 0.34, # Previous (non-P.1) infection provides 54–79% of the protection against infection with P.1 that it provides against non-P.1 lineages: https://science.sciencemag.org/content/early/2021/04/13/science.abh2644 alpha = 0.4, # Assumption based on the above beta = 0.4, # Assumption based on the above gamma = 1.0, # Default for own-immunity delta = 0.088 # Assumption ), delta = dict( # Parameters from https://www.cell.com/cell/fulltext/S0092-8674(21)00755-8 wild = 0.374, alpha = 0.689, beta = 0.086, gamma = 0.088, delta = 1.0 # Default for own-immunity ), ) return _get_from_pars(pars, default, key=variant, defaultkey='wild')
[docs]def get_vaccine_variant_pars(default=False, vaccine=None): ''' Define the effectiveness of each vaccine against each variant ''' pars = dict( default = dict( wild = 1.0, alpha = 1.0, beta = 1.0, gamma = 1.0, delta = 1.0, ), pfizer = dict( wild = 1.0, alpha = 1/2.0, # https://www.nejm.org/doi/full/10.1056/nejmc2100362 beta = 1/10.3, # https://www.nejm.org/doi/full/10.1056/nejmc2100362 gamma = 1/6.7, # https://www.nejm.org/doi/full/10.1056/nejmc2100362 delta = 1/2.9, # https://www.researchsquare.com/article/rs-637724/v1 ), moderna = dict( wild = 1.0, alpha = 1/1.8, beta = 1/4.5, gamma = 1/8.6, # https://www.nejm.org/doi/full/10.1056/nejmc2100362 delta = 1/2.9, # https://www.researchsquare.com/article/rs-637724/v1 ), az = dict( wild = 1.0, alpha = 1/2.3, beta = 1/9, gamma = 1/2.9, delta = 1/6.2, # https://www.researchsquare.com/article/rs-637724/v1 ), jj = dict( wild = 1.0, alpha = 1.0, beta = 1/3.6, # https://www.biorxiv.org/content/10.1101/2021.07.01.450707v1.full.pdf gamma = 1/3.4, # https://www.biorxiv.org/content/10.1101/2021.07.01.450707v1.full.pdf delta = 1/1.6, # https://www.biorxiv.org/content/10.1101/2021.07.01.450707v1.full.pdf ), novavax = dict( # Data from https://ir.novavax.com/news-releases/news-release-details/novavax-covid-19-vaccine-demonstrates-893-efficacy-uk-phase-3 wild = 1.0, alpha = 1/1.12, beta = 1/4.7, gamma = 1/8.6, # Assumption, no data available yet delta = 1/6.2, # Assumption, no data available yet ), sinovac = dict( wild = 1.0, alpha = 1/1.12, beta = 1/4.7, gamma = 1/8.6, # Assumption, no data available yet delta = 1/6.2, # Assumption, no data available yet ), sinopharm = dict( wild = 1.0, alpha = 1/1.12, beta = 1/4.7, gamma = 1/8.6, # Assumption, no data available yet delta = 1/6.2, # Assumption, no data available yet ) ) return _get_from_pars(pars, default=default, key=vaccine)
[docs]def get_vaccine_dose_pars(default=False, vaccine=None): ''' Define the parameters for each vaccine ''' pars = dict( default = dict( nab_init = dict(dist='normal', par1=0, par2=2), # Initial distribution of NAbs nab_boost = 2, # Factor by which a dose increases existing NABs doses = 1, # Number of doses for this vaccine interval = None, # Interval between doses ), pfizer = dict( nab_init = dict(dist='normal', par1=-1, par2=2), nab_boost = 4, doses = 2, interval = 21, ), moderna = dict( nab_init = dict(dist='normal', par1=-1, par2=2), nab_boost = 8, doses = 2, interval = 28, ), az = dict( nab_init = dict(dist='normal', par1=-1.5, par2=2), nab_boost = 2, doses = 2, interval = 21, ), jj = dict( nab_init = dict(dist='normal', par1=1, par2=2), nab_boost = 3, doses = 1, interval = None, ), novavax = dict( nab_init = dict(dist='normal', par1=-0.9, par2=2), nab_boost = 3, doses = 2, interval = 21, ), sinovac = dict( nab_init = dict(dist='normal', par1=-2, par2=2), nab_boost = 2, doses = 2, interval = 14, ), sinopharm = dict( nab_init = dict(dist='normal', par1=-1, par2=2), nab_boost = 2, doses = 2, interval = 21, ) ) return _get_from_pars(pars, default, key=vaccine)