def set_sim_prog_prob(self, params_dict):
"""
Allows for testing prognoses probability as absolute rather than relative.
NOTE: You can only call this once per test or you will overwrite your stuff.
"""
supported_probabilities = [
'rel_symp_prob', 'rel_severe_prob', 'rel_crit_prob',
'rel_death_prob'
]
if not self.sim_pars:
self.set_sim_pars()
if not self.sim_progs:
self.sim_progs = cv.get_prognoses(self.sim_pars['prog_by_age'])
for k in params_dict:
prognosis_in_question = None
expected_prob = params_dict[k]
if k == 'rel_symp_prob': prognosis_in_question = 'symp_probs'
elif k == 'rel_severe_prob': prognosis_in_question = 'severe_probs'
elif k == 'rel_crit_prob': prognosis_in_question = 'crit_probs'
elif k == 'rel_death_prob': prognosis_in_question = 'death_probs'
else:
raise KeyError(
f"Key {k} not found in {supported_probabilities}.")
old_probs = self.sim_progs[prognosis_in_question]
self.sim_progs[prognosis_in_question] = np.array([expected_prob] *
len(old_probs))
def run_sim(self,
params_dict=None,
write_results_json=False,
population_type=None):
if not self.sim_pars or params_dict: # If we need one, or have one here
self.set_sim_pars(params_dict=params_dict)
self.sim_pars['interventions'] = self.interventions
self.sim = cv.Sim(pars=self.sim_pars, datafile=None)
if not self.sim_progs:
self.sim_progs = cv.get_prognoses(self.sim_pars['prog_by_age'])
self.sim['prognoses'] = self.sim_progs
if population_type:
self.sim.update_pars(pop_type=population_type)
self.sim.run(verbose=0)
self.simulation_result = self.sim.to_json(tostring=False)
if write_results_json or self.is_debugging:
with open(self.expected_result_filename, 'w') as outfile:
json.dump(self.simulation_result,
outfile,
indent=4,
sort_keys=True)
pars = dict(
pop_size=50e3,
pop_type='hybrid',
rand_seed=1,
n_days=365,
pop_infected=10,
beta=0.016,
verbose=0.1,
iso_factor={k: 0.0
for k in 'hswc'},
quar_factor={k: 0.0
for k in 'hswc'},
)
# To change symptomatic probability
pars['prognoses'] = cv.get_prognoses()
pars['prognoses']['symp_probs'][:] = 1
# Define interventions
sd = 30
t = sc.objdict()
t.prob = cv.test_prob(start_day=sd,
symp_prob=0.06,
asymp_prob=0.0006,
symp_quar_prob=1.0,
asymp_quar_prob=1.0)
t.num = cv.test_num(start_day=sd,
daily_tests=500,
symp_test=10.0,
quar_test=10.0)
ct = cv.contact_tracing(start_day=sd, trace_probs=1, trace_time=0)
'dist': 'lognormal_int',
'par1': 14.0,
'par2': 2.4
},
'crit2rec': {
'dist': 'lognormal_int',
'par1': 14.0,
'par2': 2.4
},
'crit2die': {
'dist': 'lognormal_int',
'par1': 6.2,
'par2': 1.7
}
}
prognoses = cv.get_prognoses()
prognoses['sus_ORs'] = np.array(
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.0, 1.0, 1.0,
1.0]) # Equally susceptible
prognoses['symp_probs'] = np.array(
[0.905, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0]) # Equally susceptible
prognoses['severe_probs'] = np.array(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) # Zero'd out
prognoses['crit_probs'] = np.array(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) # Zero'd out
prognoses['death_probs'] = np.array(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) # Zero'd out
pars['prognoses'] = prognoses
pars['start_day'] = '2020-01-01'
pars['n_days'] = 60
def create_sim(pars=None, use_safegraph=True, label=None, show_intervs=False, people=None, adjust_ORs=False,
num_pos=None, test_prob=None,
trace_prob=None, NPI_schools=None, test_freq=None, network_change=False, schedule=None,
school_start_day=None,
intervention_start_day=None, ttq_scen=None
):
''' Create a single simulation for further use '''
p = sc.objdict(sc.mergedicts(define_pars(which='best', kind='both', use_safegraph=use_safegraph), pars))
if 'rand_seed' not in p:
seed = 1
print(f'Note, could not find random seed in {pars}! Setting to {seed}')
p['rand_seed'] = seed # Ensure this exists
# Basic parameters and sim creation
pars = {'pop_size': 225e3,
'pop_scale': 10,
'pop_type': 'synthpops',
'pop_infected': p.pop_infected,
'beta': p.beta,
'start_day': '2020-02-01',
'end_day': p['end_day'],
'rescale': True,
'rescale_factor': 1.1,
'verbose': 0.1,
'rand_seed': p.rand_seed,
'analyzers': [cv.age_histogram(datafile=age_data_file,
states=['exposed', 'dead', 'tested', 'diagnosed', 'severe'])],
'beta_layer': dict(h=p.bl_h, s=p.bl_s, w=p.bl_w, c=p.bl_c, l=p.bl_l),
}
# If supplied, use an existing people object
if people:
popfile = people
else:
# Generate the population filename
n_popfiles = 5
popfile = popfile_stem + str(pars['rand_seed'] % n_popfiles) + '.ppl'
popfile_change = popfile_stem_change + str(pars['rand_seed'] % n_popfiles) + '.ppl'
# Check that the population file exists
if not os.path.exists(popfile):
errormsg = f'WARNING: could not find population file {popfile}! Please regenerate first'
raise FileNotFoundError(errormsg)
# Create and initialize the sim
print(f'Creating sim! safegraph={use_safegraph}, seed={p.rand_seed}')
sim = cv.Sim(pars, label=label, popfile=popfile, load_pop=True,
datafile=epi_data_file) # Create this here so can be used for test numbers etc.
interventions = []
# Testing bins
df = pd.read_csv(age_series_file)
age_bins = [0, 20, 40, 60, 80, np.inf]
test_num_subtargs = [ftest_num_subtarg1, ftest_num_subtarg2, ftest_num_subtarg3, ftest_num_subtarg4,
ftest_num_subtarg5]
for ia in range(len(age_bins) - 1):
label = '{}-{}'.format(age_bins[ia], age_bins[ia + 1] - 1)
df_ = df[(df['age'] >= age_bins[ia]) & (df['age'] < age_bins[ia + 1])]
# sum for the dates
df_ = df_.groupby('date').sum()
df_['age'] = age_bins[ia]
df_['datetime'] = [sc.readdate(d) for d in df_.index.values]
df_ = df_.set_index('datetime')
# make sure we have all the days we care about
new_index = pd.date_range(df_.index[0], df_.index[-1], freq='1d')
df_ = df_.reindex(new_index, fill_value=0.0, method='nearest')
test_kwargs = dict(daily_tests=df_['new_tests'], quar_test=1.0, test_delay=2, subtarget=test_num_subtargs[ia])
interventions += [cv.test_num(symp_test=p.tn1, start_day='2020-01-27', end_day='2020-03-23', **test_kwargs,
label='tn1 ' + label)]
interventions += [cv.test_num(symp_test=p.tn2, start_day='2020-03-24', end_day='2020-04-14', **test_kwargs,
label='tn2 ' + label)]
interventions += [cv.test_num(symp_test=p.tn3, start_day='2020-04-15', end_day='2020-05-07', **test_kwargs,
label='tn3 ' + label)]
interventions += [cv.test_num(symp_test=p.tn4, start_day='2020-05-08', end_day='2020-06-04', **test_kwargs,
label='tn4 ' + label)]
interventions += [cv.test_num(symp_test=p.tn5, start_day='2020-06-17', end_day='2020-06-18', **test_kwargs,
label='tn5 ' + label)]
interventions += [cv.test_num(symp_test=p.tn6, start_day='2020-06-19', end_day=None, **test_kwargs,
label='tn6 ' + label)]
# Seed in LTCF
interventions += [seed_ltcf(days=[15], seeds=[p.lseeds])]
# Define beta interventions
b_days = ['2020-03-02',
'2020-03-12',
'2020-03-23',
'2020-04-15',
'2020-05-08',
'2020-06-05',
'2020-06-19'
]
# Home and school beta changes
interventions += [
cv.change_beta(days=b_days[1], changes=p.bc_s, layers='s', label="beta_s")
]
# Work and community beta changes
b_days_wc = np.arange(sim.day(b_days[0]), sim.day(b_days[1]) + 1).tolist() + b_days[2:]
b_ch_wc = np.linspace(1.0, p.bc_wc1, len(b_days_wc) - 5).tolist() + [p.bc_wc2, p.bc_wc3, p.bc_wc4, p.bc_wc5,
p.bc_wc6]
for lkey in ['w', 'c']:
interventions += [cv.change_beta(days=b_days_wc, changes=b_ch_wc, layers=lkey, label=f'beta_{lkey}')]
# LTCF beta change
b_days_l = np.arange(sim.day(b_days[0]), sim.day(b_days[2]) + 1)
b_ch_l = np.linspace(1.0, p.bc_lf, len(b_days_l))
interventions += [cv.change_beta(days=b_days_l, changes=b_ch_l, layers='l', label='beta_l')]
# sim.people.contacts['c'] = remove_ltcf_community(sim) # Remove community contacts from LTCF
# Age-based beta changes
b_age_days = ["2020-05-08", "2020-06-05", "2020-06-19"]
b_age_changes = [[p.bc_age_u10_a, # 0
p.bc_age_10_20_a, # 10
p.bc_age_20_30_a, # 20
p.bc_age_30_40_a, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_a, # 65
p.bc_age_65u_a, # 70
p.bc_age_65u_a, # 80
p.bc_age_65u_a, # 90
],
[p.bc_age_u10_b, # 0
p.bc_age_10_20_b, # 10
p.bc_age_20_30_b, # 20
p.bc_age_30_40_b, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_b, # 65
p.bc_age_65u_b, # 70
p.bc_age_65u_b, # 80
p.bc_age_65u_b, # 90
],
[p.bc_age_u10_c, # 0
p.bc_age_10_20_c, # 10
p.bc_age_20_30_c, # 20
p.bc_age_30_40_c, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_c, # 65
p.bc_age_65u_c, # 70
p.bc_age_65u_c, # 80
p.bc_age_65u_c, # 90
]
]
interventions += [beta_change_age.change_beta_age(b_age_days, b_age_changes)]
# SafeGraph intervention & tidy up
if use_safegraph:
interventions += make_safegraph(sim)
if ttq_scen == 'lower':
tp = sc.objdict(
symp_prob=0.08,
asymp_prob=0.001,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.01,
trace_time=3.0,
)
elif ttq_scen == 'medium':
tp = sc.objdict(
symp_prob=0.12,
asymp_prob=0.0015,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.25,
trace_time=3.0,
)
elif ttq_scen == 'upper':
tp = sc.objdict(
symp_prob=0.24,
asymp_prob=0.003,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.5,
trace_time=3.0,
)
if ttq_scen is not None:
interventions += [cv.test_prob(start_day='2020-07-10', **tp), cv.contact_tracing(start_day='2020-07-10', **ct)]
if network_change:
popfile_new = popfile_change
else:
popfile_new = None
if NPI_schools is not None:
interventions += [cv.change_beta(days=sim.day('2020-09-01'), changes=NPI_schools, layers='s')]
interventions += [cv.close_schools(day_schools_closed='2020-03-12', start_day=school_start_day,
pop_file=popfile_new)]
interventions += [cv.reopen_schools(start_day=intervention_start_day, num_pos=num_pos, test=test_prob,
trace=trace_prob, ili_prev=0.002, test_freq=test_freq, schedule=schedule)]
sim['interventions'] = interventions
# Don't show interventions in plots, there are too many
for interv in sim['interventions']:
interv.do_plot = False
# Prognoses (particularly to severe/hospitalizations) need attention
prognoses = sc.dcp(cv.get_prognoses())
prognoses['severe_probs'] *= prognoses[
'symp_probs'] # Conditional probability of symptoms becoming severe, given symptomatic
prognoses['crit_probs'] *= prognoses[
'severe_probs'] # Conditional probability of symptoms becoming critical, given severe
prognoses['death_probs'] *= prognoses['crit_probs'] # Conditional probability of dying, given critical symptoms
prognoses.update({'age_cutoff': np.array([0, 10, 20, 30, 40, 50, 65, 70, 80, 90])})
prognoses.update({'severe_probs': np.array([1, 1, 1, p.rsp1, p.rsp1, p.rsp1, p.rsp1, p.rsp1, p.rsp2, p.rsp2]) *
prognoses['severe_probs']})
prognoses['death_probs'] /= prognoses['crit_probs'] # Conditional probability of dying, given critical symptoms
prognoses['crit_probs'] /= prognoses[
'severe_probs'] # Conditional probability of symptoms becoming critical, given severe
prognoses['severe_probs'] /= prognoses[
'symp_probs'] # Conditional probability of symptoms becoming severe, given symptomatic
sim.pars.update({'prognoses': prognoses})
return sim