def examplev2():
pars = {'use_waning': True}
variants = [cv.variant('b117', days=30, n_imports=10)]
sim = cv.Sim(pars=pars, variants=variants)
# length of our base campaign
duration = 30
# estimate per-day probability needed for a coverage of 30%
prob = cv.historical_vaccinate_prob.estimate_prob(duration=duration,
coverage=0.30)
print('using per-day probability of ', prob)
# estimate per-day probability needed for a coverage of 30%
prob2 = cv.historical_vaccinate_prob.estimate_prob(duration=2 * duration,
coverage=0.30)
scenarios = {
'base': {
'name': 'baseline',
'pars': {}
},
'scen1': {
'name': 'historical_vaccinate',
'pars': {
'interventions': [
cv.historical_vaccinate_prob(vaccine='pfizer',
days=np.arange(-duration, 0),
prob=prob)
]
}
},
'scen2': {
'name': 'vaccinate',
'pars': {
'interventions': [
cv.vaccinate_prob(vaccine='pfizer',
days=np.arange(0, 30),
prob=prob)
]
}
},
'scen3': {
'name': 'historical_vaccinate into sim',
'pars': {
'interventions': [
cv.historical_vaccinate_prob(vaccine='pfizer',
days=np.arange(-30, 30),
prob=prob2)
]
}
},
}
scens = cv.Scenarios(sim=sim, scenarios=scenarios)
scens.run()
scens.plot()
def test_vaccines(do_plot=False):
sc.heading('Testing vaccines...')
p1 = cv.variant('sa variant', days=20, n_imports=20)
pfizer = cv.vaccinate_prob(vaccine='pfizer', days=30)
sim = cv.Sim(base_pars, use_waning=True, variants=p1, interventions=pfizer)
sim.run()
if do_plot:
sim.plot('overview-variant')
return sim
def test_vaccine_1variant(do_plot=False, do_show=True, do_save=False):
sc.heading('Test vaccination with a single variant')
pars = sc.mergedicts(base_pars, {
'beta': 0.015,
'n_days': 120,
})
pfizer = cv.vaccinate_prob(days=[20], vaccine='pfizer')
sim = cv.Sim(use_waning=True, pars=pars, interventions=pfizer)
sim.run()
return sim
def test_vaccine_1variant_scen(do_plot=False, do_show=True, do_save=False):
sc.heading('Run a basic sim with 1 variant, pfizer vaccine')
# Define baseline parameters
n_runs = 3
base_sim = cv.Sim(use_waning=True, pars=base_pars)
# Vaccinate 75+, then 65+, then 50+, then 18+ on days 20, 40, 60, 80
base_sim.vxsubtarg = sc.objdict()
base_sim.vxsubtarg.age = [75, 65, 50, 18]
base_sim.vxsubtarg.prob = [.05, .05, .05, .05]
base_sim.vxsubtarg.days = subtarg_days = [20, 40, 60, 80]
pfizer = cv.vaccinate_prob(days=subtarg_days,
vaccine='pfizer',
subtarget=vacc_subtarg)
# Define the scenarios
scenarios = {
'baseline': {
'name': 'No Vaccine',
'pars': {}
},
'pfizer': {
'name': 'Pfizer starting on day 20',
'pars': {
'interventions': [pfizer],
}
},
}
metapars = {'n_runs': n_runs}
scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios)
scens.run()
to_plot = sc.objdict({
'New infections': ['new_infections'],
'Cumulative infections': ['cum_infections'],
'New reinfections': ['new_reinfections'],
# 'Cumulative reinfections': ['cum_reinfections'],
})
if do_plot:
scens.plot(do_save=do_save,
do_show=do_show,
fig_path='results/test_basic_vaccination.png',
to_plot=to_plot)
return scens
def test_vaccine_1dose(do_plot=False, do_show=True, do_save=False):
# Create some base parameters
pars = sc.mergedicts(base_pars, {
'beta': 0.015,
'n_days': 120,
})
janssen = cv.vaccinate_prob(vaccine='janssen', days=[0])
sim = cv.Sim(use_waning=True, pars=pars, interventions=janssen)
sim.run()
to_plot = sc.objdict({
'New infections': ['new_infections'],
'Cumulative infections': ['cum_infections'],
'New reinfections': ['new_reinfections'],
})
if do_plot:
sim.plot(do_save=do_save,
do_show=do_show,
fig_path='results/test_reinfection.png',
to_plot=to_plot)
def make_sim(use_defaults=False, do_plot=False, **kwargs):
'''
Define a default simulation for testing the baseline -- use hybrid and include
interventions to increase coverage. If run directly (not via pytest), also
plot the sim by default.
'''
# Define the interventions
tp = cv.test_prob(start_day=20, symp_prob=0.1, asymp_prob=0.01)
vx = cv.vaccinate_prob('pfizer', days=30, prob=0.1)
cb = cv.change_beta(days=40, changes=0.5)
ct = cv.contact_tracing(trace_probs=0.3, start_day=50)
# Define the parameters
pars = dict(
use_waning=True, # Whether or not to use waning and NAb calculations
pop_size=20e3, # Population size
pop_infected=
100, # Number of initial infections -- use more for increased robustness
pop_type=
'hybrid', # Population to use -- "hybrid" is random with household, school,and work structure
n_days=60, # Number of days to simulate
verbose=0, # Don't print details of the run
rand_seed=2, # Set a non-default seed
interventions=[cb, tp, ct,
vx], # Include the most common interventions
)
pars = sc.mergedicts(pars, kwargs)
# Create the sim
if use_defaults:
sim = cv.Sim()
else:
sim = cv.Sim(pars)
# Optionally plot
if do_plot:
s2 = sim.copy()
s2.run()
s2.plot()
return sim
def test_synthpops():
sim = cv.Sim(use_waning=True,
**sc.mergedicts(base_pars,
dict(pop_size=5000, pop_type='synthpops')))
sim.popdict = cv.make_synthpop(sim,
with_facilities=True,
layer_mapping={'LTCF': 'f'})
sim.reset_layer_pars()
# Vaccinate 75+, then 65+, then 50+, then 18+ on days 20, 40, 60, 80
sim.vxsubtarg = sc.objdict()
sim.vxsubtarg.age = [75, 65, 50, 18]
sim.vxsubtarg.prob = [.05, .05, .05, .05]
sim.vxsubtarg.days = subtarg_days = [20, 40, 60, 80]
pfizer = cv.vaccinate_prob(days=subtarg_days,
vaccine='pfizer',
subtarget=vacc_subtarg)
sim['interventions'] += [pfizer]
sim.run()
return sim
def test_vaccine_target_eff():
sc.heading('Testing vaccine with pre-specified efficacy...')
target_eff_1 = 0.7
target_eff_2 = 0.95
default_pars = cv.parameters.get_vaccine_dose_pars(default=True)
test_pars = dict(doses=2, interval=21, target_eff=[target_eff_1, target_eff_2])
vacc_pars = sc.mergedicts(default_pars, test_pars)
# construct analyzer to select placebo arm
class placebo_arm(cv.Analyzer):
def __init__(self, day, trial_size, **kwargs):
super().__init__(**kwargs)
self.day = day
self.trial_size = trial_size
return
def initialize(self, sim=None):
self.placebo_inds = []
self.initialized = True
return
def apply(self, sim):
if sim.t == self.day:
eligible = cv.true(~np.isfinite(sim.people.date_exposed) & ~sim.people.vaccinated)
self.placebo_inds = eligible[cv.choose(len(eligible), min(self.trial_size, len(eligible)))]
return
pars = dict(
rand_seed = 1, # Note: results may be sensitive to the random seed
pop_size = 20_000,
beta = 0.01,
n_days = 90,
verbose = -1,
use_waning = True,
)
# Define vaccine arm
trial_size = 4_000
start_trial = 20
def subtarget(sim):
''' Select people who are susceptible '''
if sim.t == start_trial:
eligible = cv.true(~np.isfinite(sim.people.date_exposed))
inds = eligible[cv.choose(len(eligible), min(trial_size // 2, len(eligible)))]
else:
inds = []
return {'vals': [1.0 for ind in inds], 'inds': inds}
# Initialize
vx = cv.vaccinate_prob(vaccine=vacc_pars, days=[start_trial], label='target_eff', prob=0.0, subtarget=subtarget)
sim = cv.Sim(
pars = pars,
interventions = vx,
analyzers = placebo_arm(day=start_trial, trial_size=trial_size // 2)
)
# Run
sim.run()
print('Vaccine efficiency:')
results = sc.objdict()
vacc_inds = cv.true(sim.people.vaccinated) # Find trial arm indices, those who were vaccinated
placebo_inds = sim['analyzers'][0].placebo_inds
assert (len(set(vacc_inds).intersection(set(placebo_inds))) == 0) # Check that there is no overlap
# Calculate vaccine efficacy against infection
VE_inf = 1 - (np.isfinite(sim.people.date_exposed[vacc_inds]).sum() /
np.isfinite(sim.people.date_exposed[placebo_inds]).sum())
# Calculate vaccine efficacy against symptoms
VE_symp = 1 - (np.isfinite(sim.people.date_symptomatic[vacc_inds]).sum() /
np.isfinite(sim.people.date_symptomatic[placebo_inds]).sum())
# Calculate vaccine efficacy against severe disease
VE_sev = 1 - (np.isfinite(sim.people.date_severe[vacc_inds]).sum() /
np.isfinite(sim.people.date_severe[placebo_inds]).sum())
results['inf'] = VE_inf
results['symp'] = VE_symp
results['sev'] = VE_sev
print(f'Against: infection: {VE_inf * 100:0.2f}%, symptoms: {VE_symp * 100:0.2f}%, severity: {VE_sev * 100:0.2f}%')
# Check that actual efficacy is within 6 %age points of target
errormsg = f'Expected VE to be about {target_eff_2}, but it is {VE_symp}. Check different random seeds; this test is highly sensitive.'
assert round(abs(VE_symp-target_eff_2),2)<=0.1, errormsg
nab_init = sim['vaccine_pars']['target_eff']['nab_init']
boost = sim['vaccine_pars']['target_eff']['nab_boost']
print(f'Initial NAbs: {nab_init}')
print(f'Boost: {boost}')
return sim
'''
Illustrate simple vaccine usage
'''
import covasim as cv
# Create some base parameters
pars = dict(
beta = 0.015,
n_days = 90,
)
# Define probability based vaccination
pfizer = cv.vaccinate_prob(vaccine='pfizer', days=20, prob=0.8)
# Create and run the sim
sim = cv.Sim(pars=pars, interventions=pfizer)
sim.run()
sim.plot(['new_infections', 'cum_infections', 'new_doses', 'cum_doses'])
def make_sim(seed,
beta,
calibration=True,
future_symp_test=None,
scenario=None,
vx_scenario=None,
end_day='2021-08-31',
verbose=0):
# Set the parameters
#total_pop = 67.86e6 # UK population size
total_pop = 55.98e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 1000
beta = beta
asymp_factor = 2
contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20}
beta_layer = {'h': 3.0, 's': 1.0, 'w': 0.6, 'c': 0.3}
if end_day is None: end_day = '2021-05-05'
pars = sc.objdict(
use_waning=True,
pop_size=pop_size,
pop_infected=pop_infected,
pop_scale=pop_scale,
pop_type=pop_type,
start_day=start_day,
end_day=end_day,
beta=beta,
asymp_factor=asymp_factor,
contacts=contacts,
rescale=True,
rand_seed=seed,
verbose=verbose,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
#sim['prognoses']['sus_ORs'][0] = 0.5 # ages 0-10
#sim['prognoses']['sus_ORs'][1] = 1.0 # ages 11-20
# ADD BETA INTERVENTIONS
#sbv is transmission in schools and assumed to be 63%=0.7*90% assuming that masks are used and redyce it by 30%
#from June 2021 we will asume that it is 50% as a combination of large scale isolation of bubbles - found via seeking optimal value
sbv = 0.63
sbv_new = 0.63
beta_past = sc.odict({
'2020-02-14': [1.00, 1.00, 0.90, 0.90],
'2020-03-16': [1.00, 0.90, 0.80, 0.80],
#first lockdown starts
'2020-03-23': [1.00, 0.02, 0.20, 0.20],
#first lockdown ends
'2020-06-01': [1.00, 0.23, 0.40, 0.40],
'2020-06-15': [1.00, 0.38, 0.50, 0.50],
'2020-07-22': [1.15, 0.00, 0.30, 0.50],
'2020-07-29': [1.15, 0.00, 0.30, 0.70],
'2020-08-12': [1.15, 0.00, 0.30, 0.70],
'2020-07-19': [1.15, 0.00, 0.30, 0.70],
'2020-07-26': [1.15, 0.00, 0.30, 0.70],
#schools start in Sep 2020
'2020-09-02': [1.15, sbv, 0.50, 0.70],
'2020-10-01': [1.15, sbv, 0.40, 0.70],
'2020-10-16': [1.15, sbv, 0.40, 0.70],
#schools holiday Oct 2020
'2020-10-26': [1.15, 0.00, 0.30, 0.60],
#2nd lockdown starts
'2020-11-05': [1.15, sbv, 0.30, 0.40],
'2020-11-14': [1.15, sbv, 0.30, 0.40],
'2020-11-21': [1.15, sbv, 0.30, 0.40],
'2020-11-30': [1.15, sbv, 0.30, 0.40],
'2020-12-05': [1.15, sbv, 0.30, 0.40],
#2nd lockdown ends and opening for Christmas
'2020-12-10': [1.50, sbv, 0.40, 0.80],
'2020-12-17': [1.50, sbv, 0.40, 0.80],
'2020-12-24': [1.50, 0.00, 0.40, 0.60],
'2020-12-26': [1.50, 0.00, 0.40, 0.70],
'2020-12-31': [1.50, 0.00, 0.20, 0.70],
'2021-01-01': [1.50, 0.00, 0.20, 0.70],
#3rd lockdown starts
'2021-01-04': [1.10, 0.14, 0.20, 0.40],
'2021-01-11': [1.05, 0.14, 0.20, 0.40],
'2021-01-18': [1.05, 0.14, 0.30, 0.30],
'2021-01-30': [1.05, 0.14, 0.30, 0.30],
'2021-02-08': [1.05, 0.14, 0.30, 0.30],
'2021-02-15': [1.05, 0.00, 0.20, 0.20],
'2021-02-22': [1.05, 0.14, 0.30, 0.30],
#3rd lockdown ends and reopening starts in 4 steps
#schools open in March 2021 - step 1 part 1
'2021-03-08': [1.05, sbv, 0.30, 0.40],
'2021-03-15': [1.05, sbv, 0.30, 0.40],
'2021-03-22': [1.05, sbv, 0.30, 0.40],
#stay at home rule finishes - step 1 part 2
'2021-03-29': [1.05, 0.00, 0.40, 0.50],
'2021-04-01': [1.05, 0.00, 0.30, 0.50],
#further relaxation measures - step 2
'2021-04-12': [1.05, 0.00, 0.30, 0.40],
'2021-04-19': [1.05, sbv, 0.30, 0.40],
'2021-04-26': [1.05, sbv, 0.30, 0.40],
'2021-05-03': [1.05, sbv, 0.30, 0.40],
'2021-05-10': [1.05, sbv, 0.30, 0.40],
#some further relaxation - step 3
'2021-05-17': [1.05, sbv, 0.30, 0.50],
'2021-05-21': [1.05, sbv, 0.30, 0.50],
#May half-term
'2021-05-31': [1.05, 0.00, 0.30, 0.40],
#slight relaxation after Spring half-term
#but delay Step 3 until 19/07/2021
'2021-06-07': [1.05, sbv, 0.30, 0.50],
'2021-06-14': [1.05, sbv, 0.30, 0.50],
'2021-06-21': [1.05, sbv, 0.30, 0.50],
'2021-06-28': [1.05, sbv, 0.30, 0.50],
'2021-07-05': [1.25, sbv, 0.30, 0.50],
'2021-07-12': [1.25, sbv, 0.30, 0.50],
'2021-07-19': [1.25, 0.00, 0.30, 0.50],
'2021-07-26': [1.25, 0.00, 0.30, 0.50],
'2021-08-02': [1.25, 0.00, 0.30, 0.50],
})
if not calibration:
##no schools until 8th March but assue 20% (1 in 5) in schools between 04/01-22/02;
##model transmission remaining at schools as 14% (to account for 30% reduction due to school measures)
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April,
## society some more (stage 3) 17th May and everything (stage 4) 21st June 2021.
## Projecting until end of August 2021.
if scenario == 'Roadmap_Step3':
beta_scens = sc.odict({
'2021-06-21': [1.05, sbv, 0.40, 0.80],
'2021-06-28': [1.25, sbv, 0.40, 0.80],
'2021-07-05': [1.25, sbv, 0.40, 0.80],
'2021-07-12': [1.25, sbv, 0.40, 0.80],
'2021-07-19': [1.25, 0.00, 0.40, 0.80],
'2021-07-26': [1.25, 0.00, 0.40, 0.80],
'2021-08-02': [1.25, 0.00, 0.40, 0.80],
})
elif scenario == 'Roadmap_delayed_Step3':
beta_scens = sc.odict({
'2021-06-21': [1.25, sbv, 0.30, 0.50],
'2021-06-28': [1.25, sbv, 0.30, 0.50],
'2021-07-05': [1.25, sbv, 0.30, 0.50],
'2021-07-12': [1.25, sbv, 0.30, 0.50],
'2021-07-19': [1.25, 0.00, 0.40, 0.80],
'2021-07-26': [1.25, 0.00, 0.40, 0.80],
'2021-08-02': [1.25, 0.00, 0.40, 0.80],
})
beta_dict = sc.mergedicts(beta_past, beta_scens)
else:
beta_dict = beta_past
beta_days = list(beta_dict.keys())
h_beta = cv.change_beta(days=beta_days,
changes=[c[0] for c in beta_dict.values()],
layers='h')
s_beta = cv.change_beta(days=beta_days,
changes=[c[1] for c in beta_dict.values()],
layers='s')
w_beta = cv.change_beta(days=beta_days,
changes=[c[2] for c in beta_dict.values()],
layers='w')
c_beta = cv.change_beta(days=beta_days,
changes=[c[3] for c in beta_dict.values()],
layers='c')
# Add B.1.1351 strain from August 2020; n_imports, rel_beta and rel_severe_beta from calibration
b1351 = cv.variant('b1351',
days=np.arange(sim.day('2020-08-10'),
sim.day('2020-08-20')),
n_imports=3000)
b1351.p['rel_beta'] = 1.2
b1351.p['rel_severe_prob'] = 0.4
sim['variants'] += [b1351]
# Add Alpha strain from October 2020; n_imports, rel_beta and rel_severe_beta from calibration
b117 = cv.variant('b117',
days=np.arange(sim.day('2020-10-20'),
sim.day('2020-10-30')),
n_imports=3000)
b117.p['rel_beta'] = 1.8
b117.p['rel_severe_prob'] = 0.4
sim['variants'] += [b117]
# Add Delta strain starting middle of April 2021; n_imports, rel_beta and rel_severe_beta from calibration
b16172 = cv.variant('b16172',
days=np.arange(sim.day('2021-04-15'),
sim.day('2021-04-20')),
n_imports=4000)
b16172.p['rel_beta'] = 2.9
b16172.p['rel_severe_prob'] = 0.2
sim['variants'] += [b16172]
interventions = [h_beta, w_beta, s_beta, c_beta]
# ADD TEST AND TRACE INTERVENTIONS
tc_day = sim.day(
'2020-03-16'
) #intervention of some testing (tc) starts on 16th March and we run until 1st April when it increases
te_day = sim.day(
'2020-04-01'
) #intervention of some testing (te) starts on 1st April and we run until 1st May when it increases
tt_day = sim.day(
'2020-05-01'
) #intervention of increased testing (tt) starts on 1st May
tti_day = sim.day(
'2020-06-01'
) #intervention of tracing and enhanced testing (tti) starts on 1st June
tti_day_july = sim.day(
'2020-07-01'
) #intervention of tracing and enhanced testing (tti) at different levels starts on 1st July
tti_day_august = sim.day(
'2020-08-01'
) #intervention of tracing and enhanced testing (tti) at different levels starts on 1st August
tti_day_sep = sim.day('2020-09-01')
tti_day_oct = sim.day('2020-10-01')
tti_day_nov = sim.day('2020-11-01')
tti_day_dec = sim.day('2020-12-01')
tti_day_jan = sim.day('2021-01-01')
tti_day_feb = sim.day('2021-02-01')
tti_day_march = sim.day('2021-03-08')
tti_day_june21 = sim.day('2021-06-20')
tti_day_july21 = sim.day('2021-07-19')
tti_day_august21 = sim.day('2021-08-02')
tti_day_sep21 = sim.day('2021-09-07')
s_prob_april = 0.012
s_prob_may = 0.012
s_prob_june = 0.04769
s_prob_july = 0.04769
s_prob_august = 0.04769
s_prob_sep = 0.07769
s_prob_oct = 0.07769
s_prob_nov = 0.07769
s_prob_dec = 0.07769
s_prob_jan = 0.08769
s_prob_march = 0.08769
#for reopening in June
#s_prob_june21 = 0.19769
#for reopening in July
s_prob_june21 = 0.08769
#for reopening in July
#s_prob_july21 = 0.195069
s_prob_july21 = 0.08769
s_prob_august21 = 0.08769
s_prob_sep21 = 0.03769
#0.114=70%; 0.149=80%; 0.205=90%
if future_symp_test is None: future_symp_test = s_prob_jan
t_delay = 1.0
#isolation may-june
iso_vals = [{k: 0.2 for k in 'hswc'}]
#isolation july
iso_vals1 = [{k: 0.4 for k in 'hswc'}]
#isolation september
iso_vals2 = [{k: 0.6 for k in 'hswc'}]
#isolation october
iso_vals3 = [{k: 0.6 for k in 'hswc'}]
#isolation november
iso_vals4 = [{k: 0.2 for k in 'hswc'}]
#isolation december
iso_vals5 = [{k: 0.5 for k in 'hswc'}]
#isolation March 2021
####changed to 0.2 for fitting
iso_vals6 = [{k: 0.5 for k in 'hswc'}]
#isolation from 20 June 2021 reduced
iso_vals7 = [{k: 0.7 for k in 'hswc'}]
#isolation from 16 July 2021 increased
####chnaged to 0.2 for fitting
iso_vals8 = [{k: 0.3 for k in 'hswc'}]
#isolation from August 2021
iso_vals9 = [{k: 0.5 for k in 'hswc'}]
#isolation from Sep 2021
iso_vals10 = [{k: 0.5 for k in 'hswc'}]
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.009,
asymp_prob=0.0,
symp_quar_prob=0.0,
start_day=tc_day,
end_day=te_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_april,
asymp_prob=0.0,
symp_quar_prob=0.0,
start_day=te_day,
end_day=tt_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_may,
asymp_prob=0.00076,
symp_quar_prob=0.0,
start_day=tt_day,
end_day=tti_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_june,
asymp_prob=0.00076,
symp_quar_prob=0.0,
start_day=tti_day,
end_day=tti_day_july - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_july,
asymp_prob=0.00076,
symp_quar_prob=0.0,
start_day=tti_day_july,
end_day=tti_day_august - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_august,
asymp_prob=0.0028,
symp_quar_prob=0.0,
start_day=tti_day_august,
end_day=tti_day_sep - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_sep,
asymp_prob=0.0028,
symp_quar_prob=0.0,
start_day=tti_day_sep,
end_day=tti_day_oct - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_oct,
asymp_prob=0.0028,
symp_quar_prob=0.0,
start_day=tti_day_oct,
end_day=tti_day_nov - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_nov,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_nov,
end_day=tti_day_dec - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_dec,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_dec,
end_day=tti_day_jan - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_jan,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_jan,
end_day=tti_day_feb - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_jan,
asymp_prob=0.008,
symp_quar_prob=0.0,
start_day=tti_day_feb,
end_day=tti_day_march - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_march,
asymp_prob=0.008,
symp_quar_prob=0.0,
start_day=tti_day_march,
end_day=tti_day_june21 - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_june21,
asymp_prob=0.008,
symp_quar_prob=0.0,
start_day=tti_day_june21,
end_day=tti_day_july21 - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_july21,
asymp_prob=0.004,
symp_quar_prob=0.0,
start_day=tti_day_july21,
end_day=tti_day_august21 - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_august21,
asymp_prob=0.004,
symp_quar_prob=0.0,
start_day=tti_day_august21,
end_day=tti_day_sep21 - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_sep21,
asymp_prob=0.008,
symp_quar_prob=0.0,
start_day=tti_day_sep21,
test_delay=t_delay),
cv.contact_tracing(trace_probs={
'h': 1,
's': 0.8,
'w': 0.8,
'c': 0.1
},
trace_time={
'h': 0,
's': 1,
'w': 1,
'c': 2
},
start_day='2020-06-01',
end_day='2023-07-12',
quar_period=10),
#cv.contact_tracing(trace_probs={'h': 1, 's': 0.8, 'w': 0.8, 'c': 0.1},
# trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2},
# start_day='2021-07-12', end_day='2021-07-20',
# quar_period=10),
#cv.contact_tracing(trace_probs={'h': 1, 's': 0.8, 'w': 0.8, 'c': 0.1},
# trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2},
# start_day='2021-07-20', end_day='2022-07-19',
# quar_period=10),
#cv.contact_tracing(trace_probs={'h': 1, 's': 0.5, 'w': 0.5, 'c': 0.05},
# trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2},
# start_day='2021-03-08',
# quar_period=5),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_july,
'vals': iso_vals1
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_sep,
'vals': iso_vals2
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_oct,
'vals': iso_vals3
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_nov,
'vals': iso_vals4
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_dec,
'vals': iso_vals5
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_march,
'vals': iso_vals6
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_june21,
'vals': iso_vals7
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_july21,
'vals': iso_vals8
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_august21,
'vals': iso_vals9
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_sep21,
'vals': iso_vals10
}})
]
#cv.dynamic_pars({'rel_crit_prob': {'days': tti_day_vac, 'vals': 1.2}}),
#cv.dynamic_pars({'rel_severe_prob': {'days': tti_day_dec, 'vals': 0.7}}),
#cv.dynamic_pars({'rel_death_prob': {'days': tti_day_dec, 'vals': 1.2}})]
#cv.vaccine(days=[0,14], rel_sus=0.4, rel_symp=0.2, cumulative=[0.7, 0.3])]
dose_pars = cvp.get_vaccine_dose_pars()['az']
dose_pars['interval'] = 7 * 8
variant_pars = cvp.get_vaccine_variant_pars()['az']
az_vaccine = sc.mergedicts({'label': 'az_uk'},
sc.mergedicts(dose_pars, variant_pars))
dose_pars = cvp.get_vaccine_dose_pars()['pfizer']
dose_pars['interval'] = 7 * 8
variant_pars = cvp.get_vaccine_variant_pars()['pfizer']
pfizer_vaccine = sc.mergedicts({'label': 'pfizer_uk'},
sc.mergedicts(dose_pars, variant_pars))
# Loop over vaccination in different ages
for age in vx_ages:
vaccine = az_vaccine if (age > 40 and age < 65) else pfizer_vaccine
subtarget = subtargets[vx_scen][age]
vx_start_day = sim.day(vx_rollout[age]['start_day'])
vx_end_day = vx_start_day + vx_duration
days = np.arange(vx_start_day, vx_end_day)
#vx = cv.vaccinate(vaccine=vaccine, subtarget=subtarget, days=days)
vx = cv.vaccinate_prob(vaccine=vaccine, days=days, prob=0.01)
interventions += [vx]
analyzers = []
# add daily age stats analyzer
analyzers += [cv.daily_age_stats(edges=[0, 30, 65, 80, 100])]
#analyzers += [cv.age_histogram(datafile='uk_stats_by_age.xlsx', edges=np.concatenate([np.linspace(0, 90, 19),np.array([100])]))]
# Finally, update the parameters
sim.update_pars(interventions=interventions, analyzers=analyzers)
# Change death and critical probabilities
# interventions += [cv.dynamic_pars({'rel_death_prob':{'days':sim.day('2020-07-01'), 'vals':0.6}})]
# Finally, update the parameters
#sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
sim.initialize()
return sim
trial_size = 4000
start_trial = 20
def subtarget(sim):
''' Select people who are susceptible '''
if sim.t == start_trial:
eligible = cv.true(~np.isfinite(sim.people.date_exposed))
inds = eligible[cv.choose(len(eligible), min(trial_size//2, len(eligible)))]
else:
inds = []
return {'vals': [1.0 for ind in inds], 'inds': inds}
# Initialize
sims = []
for vaccine in vaccines:
vx = cv.vaccinate_prob(vaccine=vaccine, days=[start_trial], prob=0.0, subtarget=subtarget)
sim = cv.Sim(
label=vaccine,
use_waning=True,
pars=pars,
interventions=vx,
analyzers=placebo_arm(day=start_trial, trial_size=trial_size//2)
)
sims.append(sim)
# Run
# Run
msim = cv.MultiSim(sims)
msim.run(keep_people=True)
results = sc.objdict()
def test_vaccine_2variants_scen(do_plot=False, do_show=True, do_save=False):
sc.heading(
'Run a basic sim with b117 variant on day 10, pfizer vaccine day 20')
# Define baseline parameters
n_runs = 3
base_sim = cv.Sim(use_waning=True, pars=base_pars)
# Vaccinate 75+, then 65+, then 50+, then 18+ on days 20, 40, 60, 80
base_sim.vxsubtarg = sc.objdict()
base_sim.vxsubtarg.age = [75, 65, 50, 18]
base_sim.vxsubtarg.prob = [.01, .01, .01, .01]
base_sim.vxsubtarg.days = subtarg_days = [60, 150, 200, 220]
jnj = cv.vaccinate_prob(days=subtarg_days,
vaccine='j&j',
subtarget=vacc_subtarg)
b1351 = cv.variant('b1351', days=10, n_imports=20)
p1 = cv.variant('p1', days=100, n_imports=100)
# Define the scenarios
scenarios = {
'baseline': {
'name': 'B1351 on day 10, No Vaccine',
'pars': {
'variants': [b1351]
}
},
'b1351': {
'name': 'B1351 on day 10, J&J starting on day 60',
'pars': {
'interventions': [jnj],
'variants': [b1351],
}
},
'p1': {
'name': 'B1351 on day 10, J&J starting on day 60, p1 on day 100',
'pars': {
'interventions': [jnj],
'variants': [b1351, p1],
}
},
}
metapars = {'n_runs': n_runs}
scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios)
scens.run(debug=debug)
to_plot = sc.objdict({
'New infections': ['new_infections'],
'Cumulative infections': ['cum_infections'],
'New reinfections': ['new_reinfections'],
# 'Cumulative reinfections': ['cum_reinfections'],
})
if do_plot:
scens.plot(do_save=do_save,
do_show=do_show,
fig_path='results/test_vaccine_b1351.png',
to_plot=to_plot)
return scens
