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_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 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
beta=0.015,
n_days=90,
)
# Define the prioritization function
def prioritize_by_age(people):
return np.argsort(-people.age)
# Record the number of doses each person has received each day so
# that we can plot the rollout in this example. Without a custom
# analyzer, only the total number of doses will be recorded
n_doses = []
# Define sequence based vaccination
pfizer = cv.vaccinate_num(vaccine='pfizer',
sequence=prioritize_by_age,
num_doses=100)
sim = cv.Sim(pars=pars,
interventions=pfizer,
analyzers=lambda sim: n_doses.append(sim.people.doses.copy()))
sim.run()
pl.figure()
n_doses = np.array(n_doses)
fully_vaccinated = (n_doses == 2).sum(axis=1)
first_dose = (n_doses == 1).sum(axis=1)
pl.stackplot(sim.tvec, first_dose, fully_vaccinated)
pl.legend(['First dose', 'Fully vaccinated'])
pl.show()
# Define a function to specify the number of doses
def num_doses(sim):
if sim.t < 50:
return sim.t*10
else:
return 500
# Define the number of boosters
def num_boosters(sim):
if sim.t < 50: # None over the first 50 days
return 0
else:
return 50 # Then 100/day thereafter
# Define base vaccine
pfizer = cv.vaccinate_num(vaccine='pfizer', sequence='age', num_doses=num_doses)
# Only give boosters to people who have had 2 doses
booster_target = {'inds': lambda sim: cv.true(sim.people.doses != 2), 'vals': 0}
booster = cv.vaccinate_num(vaccine='pfizer', sequence='age', subtarget=booster_target, booster=True, num_doses=num_boosters)
# Track doses
n_doses = []
n_doses_boosters = []
# Create simple sim
sim = cv.Sim(
pars = pars,
interventions = pfizer,
)
sim.run()
def age_sequence(people):
return np.argsort(people.age)
if 'extra' in scen:
total_doses = 2.0e6
else:
total_doses = 0.0e5
extra_days = sc.daterange('2021-07-11', '2021-07-20')
doses = int(total_doses / len(extra_days))
for day in extra_days:
vxdict[day] = doses
vax = 'az' if 'az' in scen else 'pfizer'
vx = cv.vaccinate_num(
vaccine=vax,
num_doses=vxdict,
sequence=age_sequence,
label=f'{scen}') # Most vaccinations have been AstraZeneca to date
sim = cv.Sim(
pars=pars,
interventions=[vx],
label=f'{scen}',
)
sims.append(sim)
msim = cv.MultiSim(sims)
msim.run()
to_plot = cv.get_default_plots('default', 'scen')
to_plot['Doses'] = ['cum_doses']
to_plot['Infections'] = ['cum_infections']