''' Demonstrate the vaccine intervention with subtargeting '''
import covasim as cv
import numpy as np
# Define the vaccine subtargeting
def vaccinate_by_age(sim):
young = cv.true(sim.people.age < 50) # cv.true() returns indices of people matching this condition, i.e. people under 50
middle = cv.true((sim.people.age >= 50) * (sim.people.age < 75)) # Multiplication means "and" here
old = cv.true(sim.people.age > 75)
inds = sim.people.uid # Everyone in the population -- equivalent to np.arange(len(sim.people))
vals = np.ones(len(sim.people)) # Create the array
vals[young] = 0.1 # 10% probability for people <50
vals[middle] = 0.5 # 50% probability for people 50-75
vals[old] = 0.9 # 90% probbaility for people >75
output = dict(inds=inds, vals=vals)
return output
# Define the vaccine
vaccine = cv.simple_vaccine(days=20, rel_sus=0.8, rel_symp=0.06, subtarget=vaccinate_by_age)
if __name__ == '__main__':
# Create, run, and plot the simulations
sim1 = cv.Sim(label='Baseline')
sim2 = cv.Sim(interventions=vaccine, label='With age-targeted vaccine')
msim = cv.parallel([sim1, sim2])
msim.plot()
def test_all_interventions(do_plot=False):
''' Test all interventions supported by Covasim '''
sc.heading('Testing default interventions')
# Default parameters, using the random layer
pars = sc.objdict(
pop_size=1e3,
scaled_pop=10e3,
pop_infected=10,
n_days=90,
verbose=verbose,
)
hpars = sc.mergedicts(
pars,
{'pop_type': 'hybrid'}) # Some, but not all, tests require layers
rsim = cv.Sim(pars)
hsim = cv.Sim(hpars)
def make_sim(which='r', interventions=None):
''' Helper function to avoid having to recreate the sim each time '''
if which == 'r': sim = sc.dcp(rsim)
elif which == 'h': sim = sc.dcp(hsim)
sim['interventions'] = interventions
sim.initialize()
return sim
#%% Define the interventions
# 1. Dynamic pars
i1a = cv.test_prob(start_day=5, symp_prob=0.3)
i1b = cv.dynamic_pars({
'beta': {
'days': [40, 50],
'vals': [0.005, 0.015]
},
'rel_death_prob': {
'days': 30,
'vals': 2.0
}
}) # Starting day 30, make diagnosed people stop transmitting
# 2. Sequence
i2 = cv.sequence(days=[15, 30, 45],
interventions=[
cv.test_num(daily_tests=[20] * pars.n_days),
cv.test_prob(symp_prob=0.0),
cv.test_prob(symp_prob=0.2),
])
# 3. Change beta
i3a = cv.change_beta([30, 50], [0.0, 1.0], layers='h')
i3b = cv.change_beta([30, 40, 60], [0.0, 1.0, 0.5])
# 4. Clip edges -- should match the change_beta scenarios -- note that intervention i07 was removed
i4a = cv.clip_edges([30, 50], [0.0, 1.0], layers='h')
i4b = cv.clip_edges([30, 40, 60], [0.0, 1.0, 0.5])
# 5. Test number
i5 = cv.test_num(daily_tests=[100, 100, 100, 0, 0, 0] * (pars.n_days // 6))
# 6. Test probability
i6 = cv.test_prob(symp_prob=0.1)
# 7. Contact tracing
i7a = cv.test_prob(start_day=20,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=0)
i7b = cv.contact_tracing(start_day=20,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1, c=3))
# 8. Combination, with dynamically set days
def check_inf(interv, sim, thresh=10, close_day=18):
days = close_day if sim.people.infectious.sum() > thresh else np.nan
return days
i8a = cv.clip_edges(days=check_inf, changes=0.0,
layers='s') # Close schools
i8b = cv.clip_edges(days=[20, 32, 45],
changes=[0.7, 0.3, 0.9],
layers=['w', 'c']) # Reduce work and community
i8c = cv.test_prob(start_day=38,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=2) # Start testing for TTQ
i8d = cv.contact_tracing(start_day=40,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1,
c=3)) # Start tracing for TTQ
# 9. Vaccine
i9a = cv.simple_vaccine(days=20, prob=1.0, rel_sus=1.0, rel_symp=0.0)
i9b = cv.simple_vaccine(days=50, prob=1.0, rel_sus=0.0, rel_symp=0.0)
#%% Create the simulations
sims = sc.objdict()
sims.dynamic = make_sim('r', [i1a, i1b])
sims.sequence = make_sim('r', i2)
sims.change_beta1 = make_sim('h', i3a)
sims.clip_edges1 = make_sim('h', i4a) # Roughly equivalent to change_beta1
sims.change_beta2 = make_sim('r', i3b)
sims.clip_edges2 = make_sim('r', i4b) # Roughly equivalent to change_beta2
sims.test_num = make_sim('r', i5)
sims.test_prob = make_sim('r', i6)
sims.tracing = make_sim('h', [i7a, i7b])
sims.combo = make_sim('h', [i8a, i8b, i8c, i8d])
sims.vaccine = make_sim('r', [i9a, i9b])
# Run the simualations
for key, sim in sims.items():
sim.label = key
sim.run()
# Test intervention retrieval methods
sim = sims.combo
ce1, ce2 = sim.get_interventions(cv.clip_edges)
ce, tp = sim.get_interventions([0, 2])
inds = sim.get_interventions(cv.clip_edges, as_inds=True) # Returns [0,1]
assert inds == [0, 1]
sim.get_interventions('summary') # Prints a summary
# Test other intervention methods
ce.disp()
ce.shrink()
#%% Plotting
if do_plot:
for sim in sims.values():
print(f'Running {sim.label}...')
sim.plot()
fig = pl.gcf()
try:
fig.axes[0].set_title(f'Simulation: {sim.label}')
except:
pass
return
def test_states():
''' Test state consistency against state_diagram.xlsx '''
filename = 'state_diagram.xlsx'
sheets = ['Without waning', 'With waning']
indexcol = 'In ↓ you can be →'
# Load state diagram
dfs = sc.odict()
for sheet in sheets:
dfs[sheet] = pd.read_excel(filename, sheet_name=sheet)
dfs[sheet] = dfs[sheet].set_index(indexcol)
# Create and run simulation
for use_waning in [False, True]:
sc.heading(f'Testing state consistency with waning = {use_waning}')
df = dfs[use_waning] # Different states are possible with or without waning
# Parameters chosen to be midway through the sim so as few states as possible are empty
pars = dict(
pop_size = 1e3,
pop_infected = 20,
n_days = 70,
use_waning = use_waning,
verbose = 0,
interventions = [
cv.test_prob(symp_prob=0.4, asymp_prob=0.01),
cv.contact_tracing(trace_probs=0.1),
cv.simple_vaccine(days=60, prob=0.1)
]
)
sim = cv.Sim(pars).run()
ppl = sim.people
# Check states
errormsg = ''
states = df.columns.values.tolist()
for s1 in states:
for s2 in states:
if s1 != s2:
relation = df.loc[s1, s2] # e.g. df.loc['susceptible', 'exposed']
print(f'Checking {s1:13s} → {s2:13s} = {relation:2n} ... ', end='')
inds = cv.true(ppl[s1])
n_inds = len(inds)
vals2 = ppl[s2][inds]
is_true = cv.true(vals2)
is_false = cv.false(vals2)
n_true = len(is_true)
n_false = len(is_false)
if relation == 1 and n_true != n_inds:
errormsg = f'Being {s1}=True implies {s2}=True, but only {n_true}/{n_inds} people are'
print(f'× {n_true}/{n_inds} error!')
elif relation == -1 and n_false != n_inds:
errormsg = f'Being {s1}=True implies {s2}=False, but only {n_false}/{n_inds} people are'
print(f'× {n_false}/{n_inds} error!')
else:
print(f'✓ {n_true}/{n_inds}')
if errormsg:
raise RuntimeError(errormsg)
return
output = dict(inds=inds, vals=vals)
return output
# Load the sim
sim2 = cv.load('Seattle100kV' + str(version) + '.sim')
if (choice == 0):
seeds = generateSeeds(version)
if (choice == 1):
seeds = generateRandomSeeds(version)
if (choice == 2):
seeds = generateDegreeSeeds(version)
# Define the vaccine and add it to the sim
vaccine = cv.simple_vaccine(days=31 + (version * 7),
rel_sus=0.0,
rel_symp=0.02,
subtarget=vaccinateSeeds(sim2, seeds))
vaccine.vaccinations = vaccine.subtarget['vals'].astype(int)
vaccine.initialize(sim2)
sim2.pars['interventions'].append(vaccine)
# setting 'rel_trans' od vaccinated seeds ensure edge probability on outgoing edges are set to zero
for seed in seeds:
sim2.people.rel_trans[seed] = 0.0
# Let it run for a week
sim2.run(until='2020-04-30')
# Save the sim
sim2.save('Seattle100kV' + str(version + 1) + '.sim')