def test_import2variants(do_plot=False, do_show=True, do_save=False):
sc.heading('Test introducing 2 new variants partway through a sim')
b117 = cv.variant('b117', days=1, n_imports=20)
p1 = cv.variant('sa variant', days=2, n_imports=20)
sim = cv.Sim(use_waning=True,
variants=[b117, p1],
label='With imported infections',
**base_pars)
sim.run()
return sim
def test_variants(do_plot=False):
sc.heading('Testing variants...')
b117 = cv.variant('b117', days=10, n_imports=20)
p1 = cv.variant('sa variant', days=20, n_imports=20)
cust = cv.variant(label='Custom', days=40, n_imports=20, variant={'rel_beta': 2, 'rel_symp_prob': 1.6})
sim = cv.Sim(base_pars, use_waning=True, variants=[b117, p1, cust])
sim.run()
if do_plot:
sim.plot('overview-variant')
return sim
def test_msim(do_plot=False):
sc.heading('Testing multisim...')
# basic test for vaccine
b117 = cv.variant('b117', days=0)
sim = cv.Sim(use_waning=True, variants=[b117], **base_pars)
msim = cv.MultiSim(sim, n_runs=2)
msim.run()
msim.reduce()
to_plot = sc.objdict({
'Total infections': ['cum_infections'],
'New infections per day': ['new_infections'],
'New Re-infections per day': ['new_reinfections'],
})
if do_plot:
msim.plot(to_plot=to_plot,
do_save=0,
do_show=1,
legend_args={'loc': 'upper left'},
axis_args={'hspace': 0.4},
interval=35)
return msim
def test_variants(do_plot=False):
sc.heading('Testing variants...')
nabs = []
b117 = cv.variant('b117', days=10, n_imports=20)
p1 = cv.variant('beta', days=20, n_imports=20)
cust = cv.variant(label='Custom', days=40, n_imports=20, variant={'rel_beta': 2, 'rel_symp_prob': 1.6})
sim = cv.Sim(base_pars, use_waning=True, variants=[b117, p1, cust], analyzers=lambda sim: nabs.append(sim.people.nab.copy()))
sim.run()
if do_plot:
nabs = np.array(nabs).sum(axis=1)
pl.figure()
pl.plot(nabs)
pl.show()
sim.plot('overview-variant')
return sim
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 examplew1():
# run single sim
pars = {'use_waning': True, 'rand_seed': 1}
variants = [cv.variant('delta', days=15, n_imports=10)]
sim = cv.Sim(pars=pars, variants=variants)
sim['interventions'] += [
cv.historical_wave(variant='wild',
prob=[0.05, 0.05],
days_prior=[150, 50])
]
sim.run()
sim.plot()
sim.plot('variants')
def test_two_vaccines(do_plot=False):
sc.heading('Testing nab decay in simulation...')
p1 = cv.variant('sa variant', days=20, n_imports=0)
nabs = []
vac1 = cv.vaccinate_num(vaccine='pfizer', sequence=[0], num_doses=1)
vac2 = cv.vaccinate_num(vaccine='jj', sequence=[1], num_doses=1)
sim = cv.Sim(base_pars, n_days=1000, pop_size=2, pop_infected=0, rescale=False, use_waning=True, variants=p1, interventions=[vac1, vac2], analyzers=lambda sim: nabs.append(sim.people.nab.copy()))
sim.run()
if do_plot:
nabs = np.array(nabs)
print(sim.people.peak_nab)
pl.figure()
pl.plot(nabs)
def test_vaccines(do_plot=False):
sc.heading('Testing vaccines...')
nabs = []
p1 = cv.variant('beta', days=20, n_imports=20)
pfizer = cv.vaccinate(vaccine='pfizer', days=30)
sim = cv.Sim(base_pars, use_waning=True, variants=p1, interventions=pfizer, analyzers=lambda sim: nabs.append(sim.people.nab.copy()))
sim.run()
sim.shrink()
if do_plot:
nabs = np.array(nabs).sum(axis=1)
pl.figure()
pl.plot(nabs)
pl.show()
sim.plot('overview-variant')
return sim
def examplev3():
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)
scenarios = {
'scen1': {
'name': 'both doses',
'pars': {
'interventions': [
cv.historical_vaccinate_prob(vaccine='pfizer',
days=np.arange(-duration, 0),
prob=prob)
]
}
},
'scen3': {
'name': 'first dose only',
'pars': {
'interventions': [
cv.historical_vaccinate_prob(vaccine='pfizer',
days=np.arange(-duration, 0),
prob=prob,
compliance=[1.0, 0.0])
]
}
},
}
scens = cv.Scenarios(sim=sim, scenarios=scenarios)
scens.run()
to_plot = cv.get_default_plots(kind='scenarios')
to_plot.pop(2)
to_plot.update({'Cumulative doses': ['cum_vaccinated', 'cum_doses']})
scens.plot(to_plot=to_plot)
def test_import1variant(do_plot=False, do_show=True, do_save=False):
sc.heading('Test introducing a new variant partway through a sim')
variant_pars = {
'rel_beta': 1.5,
}
pars = {'beta': 0.01}
variant = cv.variant(variant_pars,
days=1,
n_imports=20,
label='Variant 2: 1.5x more transmissible')
sim = cv.Sim(use_waning=True,
pars=pars,
variants=variant,
analyzers=cv.snapshot(30, 60),
**pars,
**base_pars)
sim.run()
return sim
def test_vaccines_sequential(do_plot=False):
sc.heading('Testing sequential vaccine...')
n_days = 60
p1 = cv.variant('beta', days=20, n_imports=20)
num_doses = {i:(i**2)*(i%2==0) for i in np.arange(n_days)} # Test subtarget and fluctuating doses
n_doses = []
subtarget = dict(inds=np.arange(int(base_pars.pop_size//2)), vals=0.1)
pfizer = cv.vaccinate_num(vaccine='pfizer', sequence='age', num_doses=num_doses, subtarget=subtarget)
sim = cv.Sim(base_pars, n_days=n_days, rescale=False, use_waning=True, variants=p1, interventions=pfizer, analyzers=lambda sim: n_doses.append(sim.people.doses.copy()))
sim.run()
n_doses = np.array(n_doses)
if do_plot:
fully_vaccinated = (n_doses == 2).sum(axis=1)
first_dose = (n_doses == 1).sum(axis=1)
pl.stackplot(sim.tvec, first_dose, fully_vaccinated)
# Stacked bars by 10 year age
# At the end of the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[60]==2
df['first_dose'] = df[60]==1
df['unvaccinated'] = df[60]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
# Part-way through the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[40]==2
df['first_dose'] = df[40]==1
df['unvaccinated'] = df[40]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
return sim
def test_vaccines_sequential(do_plot=False):
sc.heading('Testing sequential vaccine...')
p1 = cv.variant('sa variant', days=20, n_imports=20)
def age_sequence(people): return np.argsort(-people.age)
n_doses = []
pfizer = cv.vaccinate_num(vaccine='pfizer', sequence=age_sequence, num_doses=lambda sim: sim.t)
sim = cv.Sim(base_pars, rescale=False, use_waning=True, variants=p1, interventions=pfizer, analyzers=lambda sim: n_doses.append(sim.people.vaccinations.copy()))
sim.run()
n_doses = np.array(n_doses)
if do_plot:
fully_vaccinated = (n_doses == 2).sum(axis=1)
first_dose = (n_doses == 1).sum(axis=1)
pl.stackplot(sim.tvec, first_dose, fully_vaccinated)
# Stacked bars by 10 year age
# At the end of the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[60]==2
df['first_dose'] = df[60]==1
df['unvaccinated'] = df[60]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
# Part-way through the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[40]==2
df['first_dose'] = df[40]==1
df['unvaccinated'] = df[40]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
return sim
def test_two_vaccines(do_plot=False):
sc.heading('Testing two vaccines...')
p1 = cv.variant('beta', days=20, n_imports=0)
nabs = []
vac1 = cv.vaccinate_num(vaccine='pfizer', sequence=[0], num_doses=1)
vac2 = cv.vaccinate_num(vaccine='jj', sequence=[1], num_doses=1)
sim = cv.Sim(base_pars, n_days=1000, pop_size=2, pop_infected=0, variants=p1, interventions=[vac1, vac2], analyzers=lambda sim: nabs.append(sim.people.nab.copy()))
# No infections, so suppress warnings
with cv.options.context(warnings='print'):
sim.run()
print('↑↑↑ Should print warning about no infections')
if do_plot:
nabs = np.array(nabs).sum(axis=1)
pl.figure()
pl.plot(nabs)
pl.show()
return sim
def make_sim(seed,
beta,
calibration=True,
future_symp_test=None,
scenario=None,
end_day='2021-10-30',
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,
rel_symp_prob=1.0,
rel_severe_prob=0.9,
rel_crit_prob=1.4,
rel_death_prob=1.2,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
#sim['prognoses']['sus_ORs'][0] = 0.5 # ages 20-30
#sim['prognoses']['sus_ORs'][1] = 1.0 # ages 20-30
# ADD BETA INTERVENTIONS
sbv = 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],
'2020-03-23': [1.29, 0.02, 0.20, 0.20],
'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': [1.29, 0.00, 0.30, 0.50],
'2020-09-02': [1.25, sbv, 0.50, 0.70],
'2020-10-01': [1.25, sbv, 0.50, 0.70],
'2020-10-16': [1.25, sbv, 0.50, 0.70],
'2020-10-26': [1.00, 0.00, 0.50, 0.70],
'2020-11-05': [1.25, sbv, 0.30, 0.40],
'2020-11-14': [1.25, sbv, 0.30, 0.40],
'2020-11-21': [1.25, sbv, 0.30, 0.40],
'2020-11-30': [1.25, sbv, 0.30, 0.40],
'2020-12-03': [1.50, sbv, 0.50, 0.70],
'2020-12-20': [1.25, 0.00, 0.20, 0.40],
'2020-12-25': [1.50, 0.00, 0.20, 0.40],
'2020-12-26': [1.50, 0.00, 0.20, 0.40],
'2020-12-31': [1.50, 0.00, 0.20, 0.40],
'2021-01-01': [1.50, 0.00, 0.20, 0.40],
'2021-01-04': [1.25, 0.14, 0.30, 0.40],
'2021-01-11': [1.25, 0.14, 0.30, 0.40],
'2021-01-18': [1.25, 0.14, 0.30, 0.40],
'2021-01-30': [1.25, 0.14, 0.30, 0.40],
'2021-02-08': [1.25, 0.14, 0.30, 0.40],
'2021-02-15': [1.25, 0.14, 0.30, 0.40],
'2021-02-22': [1.25, 0.14, 0.30, 0.40],
'2021-03-08': [1.25, sbv, 0.30, 0.50],
'2021-03-15': [1.25, sbv, 0.30, 0.50],
'2021-03-22': [1.25, sbv, 0.30, 0.50],
'2021-03-29': [1.25, 0.00, 0.30, 0.50],
'2021-04-05': [1.25, 0.00, 0.30, 0.50],
'2021-04-12': [1.25, 0.00, 0.40, 0.50],
'2021-04-19': [1.25, sbv, 0.40, 0.50],
'2021-04-26': [1.25, sbv, 0.40, 0.50],
'2021-05-03': [1.25, sbv, 0.40, 0.50],
'2021-05-10': [1.25, sbv, 0.40, 0.50],
'2021-05-17': [1.15, sbv, 0.40, 0.60],
'2021-05-21': [1.15, sbv, 0.40, 0.60],
'2021-05-28': [1.15, 0.00, 0.40, 0.60],
'2021-06-07': [1.15, sbv, 0.40, 0.60],
'2021-06-14': [1.15, sbv, 0.40, 0.60],
'2021-06-19': [1.15, sbv, 0.40, 0.60],
'2021-06-21': [1.25, sbv, 0.40, 0.70],
'2021-06-28': [1.25, sbv, 0.40, 0.70],
'2021-07-05': [1.25, sbv, 0.40, 0.70],
'2021-07-12': [1.25, sbv, 0.40, 0.70],
'2021-07-19': [1.25, 0.00, 0.50, 0.80],
'2021-07-26': [1.25, 0.00, 0.50, 0.80],
'2021-08-02': [1.25, 0.00, 0.50, 0.80],
'2021-08-16': [1.25, 0.00, 0.50, 0.80],
'2021-09-01': [1.25, 0.63, 0.70, 0.90],
'2021-09-15': [1.25, 0.63, 0.70, 0.90],
'2021-09-29': [1.25, 0.63, 0.70, 0.90],
'2021-10-13': [1.25, 0.63, 0.70, 0.90],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-15': [1.25, 0.63, 0.70, 0.90],
'2021-11-22': [1.25, 0.63, 0.70, 0.90],
'2021-12-01': [1.25, 0.63, 0.70, 0.90],
#'2021-06-21': [1.25, sbv, 0.70, 0.90],
#'2021-06-28': [1.25, sbv, 0.70, 0.90],
#'2021-07-05': [1.25, sbv, 0.70, 0.90],
#'2021-07-12': [1.25, sbv, 0.70, 0.90],
#'2021-07-19': [1.25, 0.00, 0.70, 0.90],
#'2021-07-26': [1.25, 0.00, 0.70, 0.90],
#'2021-08-02': [1.25, 0.00, 0.70, 0.90],
#'2021-08-16': [1.25, 0.00, 0.70, 0.90],
})
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 2021.
if scenario == 'Roadmap_All':
beta_scens = sc.odict({
'2021-06-21': [1.25, sbv, 0.70, 0.90],
'2021-06-28': [1.25, sbv, 0.70, 0.90],
'2021-07-05': [1.25, sbv, 0.70, 0.90],
'2021-07-12': [1.25, sbv, 0.70, 0.90],
'2021-07-19': [1.25, 0.00, 0.70, 0.90],
'2021-07-26': [1.25, 0.00, 0.70, 0.90],
'2021-08-02': [1.25, 0.00, 0.70, 0.90],
'2021-08-16': [1.25, 0.00, 0.70, 0.90],
'2021-09-01': [1.25, 0.63, 0.70, 0.90],
'2021-09-15': [1.25, 0.63, 0.70, 0.90],
'2021-09-29': [1.25, 0.63, 0.70, 0.90],
'2021-10-13': [1.25, 0.63, 0.70, 0.90],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-23': [1.25, 0.63, 0.70, 0.90],
'2021-11-30': [1.25, 0.63, 0.70, 0.90],
'2021-12-07': [1.25, 0.63, 0.70, 0.90],
'2021-12-20': [1.25, 0.02, 0.50, 0.80],
'2022-01-05': [1.25, 0.63, 0.70, 0.90],
})
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April ONLY
## NO (stage 3) 17th May and NO stage 4 21st June 2021.
## Projecting until end of 2021.
#elif scenario == 'Roadmap_Stage2':
#beta_scens = sc.odict({'2021-04-12': [1.25, 0.02, 0.40, 0.70],
# '2021-04-19': [1.25, sbv, 0.40, 0.70],
# '2021-04-26': [1.25, sbv, 0.40, 0.70],
# '2021-05-03': [1.25, sbv, 0.40, 0.70],
# '2021-05-10': [1.25, sbv, 0.40, 0.70],
# '2021-05-17': [1.25, sbv, 0.40, 0.70],
# '2021-05-21': [1.25, sbv, 0.40, 0.70],
# '2021-05-28': [1.25, 0.02, 0.40, 0.70],
# '2021-06-07': [1.25, sbv, 0.40, 0.70],
# '2021-06-21': [1.25, sbv, 0.40, 0.70],
# '2021-06-28': [1.25, sbv, 0.40, 0.70],
# '2021-07-05': [1.25, sbv, 0.40, 0.70],
# '2021-07-12': [1.25, sbv, 0.40, 0.70],
# '2021-07-19': [1.25, 0.00, 0.40, 0.70],
# '2021-07-26': [1.25, 0.00, 0.40, 0.70],
# '2021-08-02': [1.25, 0.00, 0.40, 0.70],
# '2021-08-16': [1.25, 0.00, 0.40, 0.70],
# '2021-09-01': [1.25, 0.63, 0.70, 0.90],
# '2021-09-15': [1.25, 0.63, 0.70, 0.90],
# '2021-09-29': [1.25, 0.63, 0.70, 0.90],
# '2021-10-13': [1.25, 0.63, 0.70, 0.90],
# '2021-10-27': [1.25, 0.02, 0.70, 0.90],
# '2021-11-08': [1.25, 0.63, 0.70, 0.90],
# '2021-11-23': [1.25, 0.63, 0.70, 0.90],
# '2021-11-30': [1.25, 0.63, 0.70, 0.90],
# '2021-12-07': [1.25, 0.63, 0.70, 0.90],
# '2021-12-21': [1.25, 0.63, 0.70, 0.90],
# })
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April,
## and society some more (stage 3) 17th May but NO stage 4 21st June 2021.
## Projecting until end of 2021.
elif scenario == 'Roadmap_Stage3':
beta_scens = sc.odict({
'2021-06-21': [1.25, sbv, 0.40, 0.60],
'2021-06-28': [1.25, sbv, 0.40, 0.60],
'2021-07-05': [1.25, sbv, 0.40, 0.60],
'2021-07-12': [1.25, sbv, 0.40, 0.60],
'2021-07-19': [1.25, 0.00, 0.50, 0.80],
'2021-07-26': [1.25, 0.00, 0.50, 0.80],
'2021-08-02': [1.25, 0.00, 0.50, 0.80],
'2021-08-16': [1.25, 0.00, 0.50, 0.80],
'2021-09-01': [1.25, 0.63, 0.70, 0.90],
'2021-09-15': [1.25, 0.63, 0.70, 0.90],
'2021-09-29': [1.25, 0.63, 0.70, 0.90],
'2021-10-13': [1.25, 0.63, 0.70, 0.90],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-23': [1.25, 0.63, 0.70, 0.90],
'2021-11-30': [1.25, 0.63, 0.70, 0.90],
'2021-12-07': [1.25, 0.63, 0.70, 0.90],
'2021-12-20': [1.25, 0.02, 0.50, 0.80],
'2022-01-05': [1.25, 0.63, 0.70, 0.90],
})
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.1.117 strain
b117 = cv.variant('b117',
days=np.arange(sim.day('2020-08-01'),
sim.day('2020-08-10')),
n_imports=500)
b117.p['rel_beta'] = 1.5
b117.p['rel_crit_prob'] = 1.3
b117.p['rel_death_prob'] = 1.6
b117.p['rel_severe_prob'] = 0.3
sim['variants'] += [b117]
# Add B.1.1351 strain
b1351 = cv.variant('b1351',
days=np.arange(sim.day('2020-01-10'),
sim.day('2020-01-20')),
n_imports=500)
b1351.p['rel_beta'] = 1.0
b1351.p['rel_death_prob'] = 1.0
b1351.p['rel_severe_prob'] = 1.0
sim['variants'] += [b1351]
# Add B.X.XXX strain starting middle of March
custom_strain = cv.variant(label='delta',
variant=cvp.get_variant_pars()['p1'],
days=np.arange(sim.day('2021-03-17'),
sim.day('2021-03-24')),
n_imports=2000)
custom_strain.p['rel_beta'] = 2.2
custom_strain.p['rel_crit_prob'] = 1.3
custom_strain.p['rel_death_prob'] = 1.1
custom_strain.p['rel_severe_prob'] = 1.0
sim['variants'] += [custom_strain]
# seems like we need to do this to deal with cross immunity?
sim.initialize()
sim['immunity']
prior = {'wild': 0.8, 'b117': 0.8, 'b1351': 0.8, 'delta': 0.8}
pre = {'wild': 0.8, 'b117': 0.8, 'b1351': 0.8, 'delta': 0.8}
for k, v in sim['variant_map'].items():
if v == 'delta':
for j, j_lab in sim['variant_map'].items():
sim['immunity'][k][j] = prior[j_lab]
sim['immunity'][j][k] = pre[j_lab]
#if j != k:
# sim['immunity'][k][j] = cross_immunities['b117'][j_lab]
# sim['immunity'][j][k] = cross_immunities[j_lab]['b117']
# # Add a new change in beta to represent the takeover of the novel variant VOC B117 202012/01
# # Assume that the new variant is 60% more transmisible (https://cmmid.github.io/topics/covid19/uk-novel-variant.html,
# # Assume that between Nov 1 and Jan 30, the new variant grows from 0-100% of cases
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_april = sim.day('2021-03-01')
#start of vaccinating those 75years+
tti_day_vac1 = sim.day('2021-01-03')
#start of vaccinating 60+ old
tti_day_vac2 = sim.day('2021-02-03')
#start of vaccinating 55+ years old
tti_day_vac3 = sim.day('2021-02-28')
#start of vaccination 50+ years old
tti_day_vac4 = sim.day('2021-03-10')
#start vaccinating of 45+
tti_day_vac5 = sim.day('2021-03-30')
#start vaccinating of 40+
tti_day_vac6 = sim.day('2021-04-20')
#start vaccinating of 35+
tti_day_vac7 = sim.day('2021-05-05')
#start vaccinating of 30+
tti_day_vac8 = sim.day('2021-05-30')
#start vaccinating of 25+
tti_day_vac9 = sim.day('2021-06-10')
#start vaccinating of 18+
tti_day_vac10 = sim.day('2021-06-30')
#start vaccinating of 11-17
tti_day_vac11 = sim.day('2021-07-10')
s_prob_april = 0.009
s_prob_may = 0.012
s_prob_june = 0.02769
s_prob_july = 0.02769
s_prob_august = 0.03769
tn = 0.09
s_prob_sep = 0.08769
s_prob_oct = 0.08769
s_prob_nov = 0.08769
s_prob_dec = 0.08769
s_prob_jan = 0.08769
#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-july
iso_vals = [{k: 0.1 for k in 'hswc'}]
#isolation august
iso_vals1 = [{k: 0.7 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: 0.5 for k in 'hswc'}]
#isolation december
iso_vals5 = [{k: 0.4 for k in 'hswc'}]
#isolation January-April
#iso_vals6 = [{k:0.3 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_jan,
asymp_prob=0.008,
symp_quar_prob=0.0,
start_day=tti_day_march,
end_day=tti_day_april - 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_april,
test_delay=t_delay),
cv.contact_tracing(trace_probs={
'h': 1,
's': 0.8,
'w': 0.8,
'c': 0.05
},
trace_time={
'h': 0,
's': 1,
'w': 1,
'c': 2
},
start_day='2020-06-01',
end_day='2023-06-30',
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_august,
'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({'rel_death_prob': {'days': tti_day_vac, 'vals': 0.9}})]
#cv.vaccine(days=[0,14], rel_sus=0.4, rel_symp=0.2, cumulative=[0.7, 0.3])]
# derived from AZ default params (3.0.2) with increased interval between doses)
# dose_pars = cvp.get_vaccine_dose_pars()['az']
# dose_pars.update({'nab_interval': 14, 'interval':7*9})
# strain_pars = cvp.get_vaccine_strain_pars()['az']
# hard code them
dose_pars = cvp.get_vaccine_dose_pars()['az']
dose_pars['interval'] = 7 * 12
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 * 12
variant_pars = cvp.get_vaccine_variant_pars()['pfizer']
pfizer_vaccine = sc.mergedicts({'label': 'pfizer_uk'},
sc.mergedicts(dose_pars, variant_pars))
# age targeted vaccination
#def subtarget_75_100(sim):
# inds = cv.true(sim.people.age >= 75)
# return {'inds': inds, 'vals': 0.020*np.ones(len(inds))}
#interventions += [utils_vac.vaccinate(vaccine=vaccine, prob=0.1, subtarget=subtarget_75_100,
# days=np.arange(sim.day('2020-12-20'), sim.day('2023-01-01')))]
# age targeted vaccination
def subtarget_75_100(sim):
inds = cv.true(sim.people.age >= 75)
if not hasattr(sim, 'subtarget_75_100'):
sim.subtarget_75_100 = cv.binomial_filter(
0.05, inds) # 95% of 70+ years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_75_100)
return {'inds': inds, 'vals': 0.02 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
subtarget=subtarget_75_100,
days=np.arange(sim.day('2020-12-20'),
sim.day('2021-09-15')))
]
def subtarget_60_75(sim):
inds = cv.true((sim.people.age >= 60) & (sim.people.age < 75))
if not hasattr(sim, 'subtarget_60_75'):
sim.subtarget_60_75 = cv.binomial_filter(
0.05, inds) # 95% of 60+ years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_60_75)
return {'inds': inds, 'vals': 0.02 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
subtarget=subtarget_60_75,
days=np.arange(sim.day('2021-01-28'),
sim.day('2021-09-15')))
]
def subtarget_50_60(sim):
inds = cv.true((sim.people.age >= 50) & (sim.people.age < 60))
if not hasattr(sim, 'subtarget_50_60'):
sim.subtarget_50_60 = cv.binomial_filter(
0.1, inds) # 90% of 50-60 years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_50_60)
return {'inds': inds, 'vals': 0.005 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=az_vaccine,
subtarget=subtarget_50_60,
days=np.arange(sim.day('2021-02-10'),
sim.day('2021-09-15')))
]
#def subtarget_40_50(sim):
# inds = cv.true((sim.people.age >= 40) & (sim.people.age < 50))
# if not hasattr(sim, 'subtarget_40_50'):
# sim.subtarget_40_50 = cv.binomial_filter(0.07, inds) # 93% of 40-50 years olds vaccinated
# inds = np.setdiff1d(inds, sim.subtarget_40_50)
# return {'inds': inds, 'vals': 0.005*np.ones(len(inds))}
#interventions += [cv.vaccinate(vaccine=az_vaccine, prob=0.1, subtarget=subtarget_40_50,
# days=np.arange(sim.day('2021-04-10'), sim.day('2023-01-01')))]
def subtarget_45_50(sim):
inds = cv.true((sim.people.age >= 45) & (sim.people.age < 50))
if not hasattr(sim, 'subtarget_45_50'):
sim.subtarget_45_50 = cv.binomial_filter(
0.1, inds) # 90% of 45-50 years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_45_50)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=az_vaccine,
subtarget=subtarget_45_50,
days=np.arange(sim.day('2021-03-20'),
sim.day('2021-09-15')))
]
def subtarget_40_45(sim):
inds = cv.true((sim.people.age >= 40) & (sim.people.age < 45))
if not hasattr(sim, 'subtarget_40_45'):
sim.subtarget_40_45 = cv.binomial_filter(
0.1, inds) # 90% of 40-45 years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_40_45)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=az_vaccine,
subtarget=subtarget_40_45,
days=np.arange(sim.day('2021-04-10'),
sim.day('2021-09-15')))
]
def subtarget_30_40(sim):
inds = cv.true((sim.people.age >= 30) & (sim.people.age < 40))
if not hasattr(sim, 'subtarget_30_40'):
sim.subtarget_30_40 = cv.binomial_filter(
0.1, inds) # 90% of 30-40 years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_30_40)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
subtarget=subtarget_30_40,
days=np.arange(sim.day('2021-05-10'),
sim.day('2021-09-15')))
]
def subtarget_25_30(sim):
inds = cv.true((sim.people.age >= 25) & (sim.people.age < 30))
if not hasattr(sim, 'subtarget_25_30'):
sim.subtarget_25_30 = cv.binomial_filter(
0.1, inds) # 90% of 25-30 years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_25_30)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
subtarget=subtarget_25_30,
days=np.arange(sim.day('2021-06-10'),
sim.day('2021-09-15')))
]
def subtarget_18_25(sim):
inds = cv.true((sim.people.age >= 18) & (sim.people.age < 25))
if not hasattr(sim, 'subtarget_18_30'):
sim.subtarget_18_25 = cv.binomial_filter(
0.1, inds) # 90% of 18-25 years vaccinated
inds = np.setdiff1d(inds, sim.subtarget_18_25)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
subtarget=subtarget_18_25,
days=np.arange(sim.day('2021-06-20'),
sim.day('2021-09-15')))
]
def subtarget_16_17(sim):
inds = cv.true((sim.people.age >= 16) & (sim.people.age < 17))
if not hasattr(sim, 'subtarget_16_17'):
sim.subtarget_16_17 = cv.binomial_filter(
0.9, inds) # 10% of 16-18 years olds vaccinated
inds = np.setdiff1d(inds, sim.subtarget_16_17)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
subtarget=subtarget_16_17,
days=np.arange(sim.day('2021-06-20'),
sim.day('2021-09-15')))
]
analyzers = []
# add daily age stats analyzer
analyzers += [
cv.daily_age_stats(edges=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
]
#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
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
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(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
# '2021-12-07': [1.25, 0.63, 0.70, 0.90],
# '2021-12-20': [1.25, 0.02, 0.50, 0.80],
# '2022-01-05': [1.25, 0.63, 0.70, 0.90],
})
#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.1.117 strain
b117 = cv.variant('b117', days=np.arange(sim.day('2020-08-20'), sim.day('2020-08-30')), n_imports=500)
b117.p['rel_beta'] = 1.6
b117.p['rel_symp_prob'] = 1.1
b117.p['rel_severe_prob'] = 0.6
b117.p['rel_crit_prob'] = 2.7
b117.p['rel_death_prob'] = 0.6
sim['variants'] += [b117]
# Add B.1.1351 strain
b1351 = cv.variant('b1351', days=np.arange(sim.day('2021-01-10'), sim.day('2021-01-20')), n_imports=1500)
b1351.p['rel_beta'] = 1.0
b1351.p['rel_severe_prob'] = 1.0
b1351.p['rel_crit_prob'] = 1.0
b1351.p['rel_death_prob'] = 1.0
sim['variants'] += [b1351]
# Add B.X.XXX strain starting middle of March
b16172 = cv.variant('b16172', days=np.arange(sim.day('2021-03-17'), sim.day('2021-03-20')), n_imports=1000)
import numpy as np
import sciris as sc
assert sc.__version__ > '1.2.0'
import covasim as cv
if __name__ == '__main__':
pars = sc.objdict(
pop_infected=0,
n_agents=1e5,
pop_scale=10,
start_day='2021-06-01',
end_day='2021-10-01',
use_waning=True,
variants=cv.variant('delta',
days='2021-06-01',
n_imports=10,
rescale=False),
)
sims = []
for scen in ['baseline', 'vx_extra_pfizer', 'vx_extra_az']:
vxdict = {}
def age_sequence(people):
return np.argsort(people.age)
if 'extra' in scen:
total_doses = 2.0e6
else:
def test_varyingimmunity(do_plot=False, do_show=True, do_save=False):
sc.heading('Test varying properties of immunity')
# Define baseline parameters
n_runs = 3
base_sim = cv.Sim(use_waning=True, n_days=400, pars=base_pars)
# Define the scenarios
b1351 = cv.variant('b1351', days=100, n_imports=20)
scenarios = {
'baseline': {
'name': 'Default Immunity (decay at log(2)/90)',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 90,
decay_time1=250,
decay_rate2=0.001),
},
},
'faster_immunity': {
'name': 'Faster Immunity (decay at log(2)/30)',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 30,
decay_time1=250,
decay_rate2=0.001),
},
},
'baseline_b1351': {
'name': 'Default Immunity (decay at log(2)/90), B1351 on day 100',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 90,
decay_time1=250,
decay_rate2=0.001),
'variants': [b1351],
},
},
'faster_immunity_b1351': {
'name': 'Faster Immunity (decay at log(2)/30), B1351 on day 100',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 30,
decay_time1=250,
decay_rate2=0.001),
'variants': [b1351],
},
},
}
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'],
'New re-infections': ['new_reinfections'],
'Population Nabs': ['pop_nabs'],
'Population Immunity': ['pop_protection'],
})
if do_plot:
scens.plot(do_save=do_save,
do_show=do_show,
fig_path='results/test_basic_immunity.png',
to_plot=to_plot)
return scens
def make_sim(seed, beta, calibration=True, future_symp_test=None, scenario=None, vx_scenario=None, end_day='2021-10-30', 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,
rel_symp_prob = 0.9,
rel_severe_prob = 0.7,
rel_crit_prob = 4.7,
rel_death_prob=1.2,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
#sim['prognoses']['sus_ORs'][0] = 0.5 # ages 20-30
#sim['prognoses']['sus_ORs'][1] = 1.0 # ages 20-30
# 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 = 0.90
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],
#schools start in Sep 2020
'2020-09-02': [1.15, sbv, 0.50, 0.70],
'2020-10-01': [1.15, sbv, 0.40, 0.60],
'2020-10-16': [1.15, sbv, 0.40, 0.60],
#schools holiday Oct 2020
'2020-10-26': [1.15, 0.00, 0.30, 0.50],
#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],
'2020-12-03': [1.15, sbv, 0.30, 0.40],
#2nd lockdown ends and opening for Christmas
'2020-12-10': [1.50, 0.00, 0.40, 0.70],
'2020-12-17': [1.50, 0.00, 0.40, 0.70],
'2020-12-25': [1.50, 0.00, 0.40, 0.70],
'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, 0.30, 0.30],
#3rd lockdown starts
'2021-01-04': [1.10, 0.14, 0.20, 0.30],
'2021-01-11': [1.05, 0.14, 0.20, 0.30],
'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, 0.30, 0.40],
'2021-04-05': [1.05, 0.00, 0.30, 0.40],
#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
'2021-05-28': [1.05, 0.00, 0.30, 0.40],
'2021-06-07': [1.05, sbv, 0.30, 0.60],
'2021-06-14': [1.05, sbv, 0.30, 0.60],
#full relaxing of social distancing - step 4 - delayed to 19/07/2021
#but we needed to increase in the model to fit data
#note sporting events open and at the end of June
#large surge in cases from middle of June
'2021-06-19': [1.05, 0.50, 0.40, 0.80],
'2021-06-21': [1.05, 0.50, 0.40, 0.80],
'2021-06-28': [1.05, 0.50, 0.40, 0.80],
'2021-07-05': [1.05, 0.50, 0.40, 0.80],
'2021-07-12': [1.05, 0.50, 0.40, 0.80],
#cases start to drop from midle of July
'2021-07-16': [1.05, 0.00, 0.30, 0.50],
#easing of socal distancing measures - delayed step 4
'2021-07-26': [1.05, 0.00, 0.30, 0.50],
'2021-08-02': [1.05, 0.00, 0.30, 0.50],
'2021-08-09': [1.05, 0.00, 0.30, 0.50],
'2021-08-16': [1.05, 0.00, 0.30, 0.50],
'2021-08-23': [1.05, 0.00, 0.30, 0.50],
#reopening schools in Sep 2021
'2021-09-01': [1.05, sbv_new, 0.60, 0.80],
'2021-09-15': [1.05, sbv_new, 0.60, 0.80],
'2021-09-29': [1.05, sbv_new, 0.60, 0.80],
'2021-10-13': [1.05, sbv_new, 0.60, 0.80],
'2021-10-22': [1.05, 0.00, 0.60, 0.80],
'2021-11-01': [1.05, sbv_new, 0.60, 0.80],
'2021-11-08': [1.05, sbv_new, 0.60, 0.80],
'2021-11-15': [1.05, sbv_new, 0.60, 0.80],
'2021-11-22': [1.05, sbv_new, 0.60, 0.80],
'2021-12-01': [1.05, sbv_new, 0.60, 0.80],
#'2021-06-21': [1.25, sbv, 0.70, 0.90],
#'2021-06-28': [1.25, sbv, 0.70, 0.90],
#'2021-07-05': [1.25, sbv, 0.70, 0.90],
#'2021-07-12': [1.25, sbv, 0.70, 0.90],
#'2021-07-19': [1.25, 0.00, 0.70, 0.90],
#'2021-07-26': [1.25, 0.00, 0.70, 0.90],
#'2021-08-02': [1.25, 0.00, 0.70, 0.90],
#'2021-08-16': [1.25, 0.00, 0.70, 0.90],
})
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 2021.
if scenario == 'Roadmap_All':
beta_scens = sc.odict({'2021-06-21': [1.25, sbv, 0.70, 0.90],
'2021-06-28': [1.25, sbv, 0.70, 0.90],
'2021-07-05': [1.25, sbv, 0.70, 0.90],
'2021-07-12': [1.25, sbv, 0.70, 0.90],
'2021-07-19': [1.25, 0.00, 0.70, 0.90],
'2021-07-26': [1.25, 0.00, 0.70, 0.90],
'2021-08-02': [1.25, 0.00, 0.70, 0.90],
'2021-08-16': [1.25, 0.00, 0.70, 0.90],
'2021-09-01': [1.25, 0.63, 0.70, 0.90],
'2021-09-15': [1.25, 0.63, 0.70, 0.90],
'2021-09-29': [1.25, 0.63, 0.70, 0.90],
'2021-10-13': [1.25, 0.63, 0.70, 0.90],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-23': [1.25, 0.63, 0.70, 0.90],
'2021-11-30': [1.25, 0.63, 0.70, 0.90],
'2021-12-07': [1.25, 0.63, 0.70, 0.90],
'2021-12-20': [1.25, 0.02, 0.50, 0.80],
'2022-01-05': [1.25, 0.63, 0.70, 0.90],
})
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April ONLY
## NO (stage 3) 17th May and NO stage 4 21st June 2021.
## Projecting until end of 2021.
#elif scenario == 'Roadmap_Stage2':
#beta_scens = sc.odict({'2021-04-12': [1.25, 0.02, 0.40, 0.70],
# '2021-04-19': [1.25, sbv, 0.40, 0.70],
# '2021-04-26': [1.25, sbv, 0.40, 0.70],
# '2021-05-03': [1.25, sbv, 0.40, 0.70],
# '2021-05-10': [1.25, sbv, 0.40, 0.70],
# '2021-05-17': [1.25, sbv, 0.40, 0.70],
# '2021-05-21': [1.25, sbv, 0.40, 0.70],
# '2021-05-28': [1.25, 0.02, 0.40, 0.70],
# '2021-06-07': [1.25, sbv, 0.40, 0.70],
# '2021-06-21': [1.25, sbv, 0.40, 0.70],
# '2021-06-28': [1.25, sbv, 0.40, 0.70],
# '2021-07-05': [1.25, sbv, 0.40, 0.70],
# '2021-07-12': [1.25, sbv, 0.40, 0.70],
# '2021-07-19': [1.25, 0.00, 0.40, 0.70],
# '2021-07-26': [1.25, 0.00, 0.40, 0.70],
# '2021-08-02': [1.25, 0.00, 0.40, 0.70],
# '2021-08-16': [1.25, 0.00, 0.40, 0.70],
# '2021-09-01': [1.25, 0.63, 0.70, 0.90],
# '2021-09-15': [1.25, 0.63, 0.70, 0.90],
# '2021-09-29': [1.25, 0.63, 0.70, 0.90],
# '2021-10-13': [1.25, 0.63, 0.70, 0.90],
# '2021-10-27': [1.25, 0.02, 0.70, 0.90],
# '2021-11-08': [1.25, 0.63, 0.70, 0.90],
# '2021-11-23': [1.25, 0.63, 0.70, 0.90],
# '2021-11-30': [1.25, 0.63, 0.70, 0.90],
# '2021-12-07': [1.25, 0.63, 0.70, 0.90],
# '2021-12-21': [1.25, 0.63, 0.70, 0.90],
# })
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April,
## and society some more (stage 3) 17th May but NO stage 4 21st June 2021.
## Projecting until end of 2021.
elif scenario == 'Roadmap_Stage3':
beta_scens = sc.odict({'2021-06-21': [1.25, sbv, 0.40, 0.60],
'2021-06-28': [1.25, sbv, 0.40, 0.60],
'2021-07-05': [1.25, sbv, 0.40, 0.60],
'2021-07-12': [1.25, sbv, 0.40, 0.60],
'2021-07-19': [1.25, 0.00, 0.50, 0.80],
'2021-07-26': [1.25, 0.00, 0.50, 0.80],
'2021-08-02': [1.25, 0.00, 0.50, 0.80],
'2021-08-16': [1.25, 0.00, 0.50, 0.80],
'2021-09-01': [1.25, 0.63, 0.70, 0.90],
'2021-09-15': [1.25, 0.63, 0.70, 0.90],
'2021-09-29': [1.25, 0.63, 0.70, 0.90],
'2021-10-13': [1.25, 0.63, 0.70, 0.90],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-23': [1.25, 0.63, 0.70, 0.90],
'2021-11-30': [1.25, 0.63, 0.70, 0.90],
'2021-12-07': [1.25, 0.63, 0.70, 0.90],
'2021-12-20': [1.25, 0.02, 0.50, 0.80],
'2022-01-05': [1.25, 0.63, 0.70, 0.90],
})
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.1.117 strain
b117 = cv.variant('b117', days=np.arange(sim.day('2020-08-20'), sim.day('2020-08-30')), n_imports=500)
b117.p['rel_beta'] = 1.6
b117.p['rel_symp_prob'] = 1.1
b117.p['rel_severe_prob'] = 0.6
b117.p['rel_crit_prob'] = 2.7
b117.p['rel_death_prob'] = 0.6
#b117.p['rel_severe_prob'] = 0.7
#b117.p['rel_crit_prob'] = 1.0
#b117.p['rel_death_prob'] = 1.0
sim['variants'] += [b117]
# Add B.1.1351 strain
b1351 = cv.variant('b1351', days=np.arange(sim.day('2021-01-10'), sim.day('2021-01-20')), n_imports=1500)
b1351.p['rel_beta'] = 1.0
b1351.p['rel_severe_prob'] = 1.0
b1351.p['rel_crit_prob'] = 1.0
b1351.p['rel_death_prob'] = 1.0
sim['variants'] += [b1351]
# Add B.X.XXX strain starting middle of March
b16172 = cv.variant('b16172', days=np.arange(sim.day('2021-03-17'), sim.day('2021-03-20')), n_imports=1000)
b16172.p['rel_beta'] = 2.1
b16172.p['rel_symp_prob'] = 0.4
b16172.p['rel_severe_prob'] = 0.1
b16172.p['rel_crit_prob'] = 0.2
b16172.p['rel_death_prob'] = 0.4
#b16172.p['rel_severe_prob'] = 0.7
#b16172.p['rel_crit_prob'] = 1.0
#b16172.p['rel_death_prob'] = 0.7
sim['variants'] += [b16172]
# seems like we need to do this to deal with cross immunity?
#sim.initialize()
#sim['immunity']
#prior = {'wild': 0.8, 'b117': 0.5, 'b1351': 0.8, 'b16172': 0.8}
#pre = {'wild': 0.8, 'b117': 0.5, 'b1351': 0.8, 'b16172': 0.8}
#for k,v in sim['variant_map'].items():
# if v == 'b16172':
# for j, j_lab in sim['variant_map'].items():
# sim['immunity'][k][j] = prior[j_lab]
# sim['immunity'][j][k] = pre[j_lab]
#if j != k:
# sim['immunity'][k][j] = cross_immunities['b117'][j_lab]
# sim['immunity'][j][k] = cross_immunities[j_lab]['b117']
# # Add a new change in beta to represent the takeover of the novel variant VOC B117 202012/01
# # Assume that the new variant is 60% more transmisible (https://cmmid.github.io/topics/covid19/uk-novel-variant.html,
# # Assume that between Nov 1 and Jan 30, the new variant grows from 0-100% of cases
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_april= sim.day('2021-03-01')
tti_day_july21= sim.day('2021-07-20')
tti_day_august21= sim.day('2021-08-15')
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
# tn = 0.09
s_prob_sep = 0.07769
s_prob_oct = 0.07769
s_prob_nov = 0.07769
s_prob_dec = 0.07769
s_prob_jan = 0.07769
s_prob_march = 0.07769
s_prob_july21 = 0.032769
s_prob_august21 = 0.05769
#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:0.5 for k in 'hswc'}]
#isolation december
iso_vals5 = [{k:0.4 for k in 'hswc'}]
#isolation July 2021
iso_vals6 = [{k:0.4 for k in 'hswc'}]
#isolation from August 2021
#iso_vals7 = [{k:0.4 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_april-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_april, 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=0.008, symp_quar_prob=0.0, start_day=tti_day_august21, test_delay=t_delay),
cv.contact_tracing(trace_probs={'h': 1, 's': 0.8, 'w': 0.8, 'c': 0.05},
trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2},
start_day='2020-06-01', end_day='2023-06-30',
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_july21, 'vals': iso_vals6}})]
#cv.dynamic_pars({'iso_factor': {'days': tti_day_august21, 'vals': iso_vals7}})]
#cv.dynamic_pars({'rel_death_prob': {'days': tti_day_vac, 'vals': 0.9}})]
#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(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
'''
Illustrate multiple variants
'''
import covasim as cv
# Define three new variants: B117, B1351, and a custom-defined variant
alpha = cv.variant('alpha', days=0, n_imports=10)
beta = cv.variant('beta', days=0, n_imports=10)
custom = cv.variant(label='3x more transmissible',
variant={'rel_beta': 3.0},
days=7,
n_imports=10)
# Create the simulation
sim = cv.Sim(variants=[alpha, beta, custom], pop_infected=10, n_days=32)
# Run and plot
sim.run()
sim.plot('variant')
