def test_all_interventions():
''' Test all interventions supported by Covasim '''
pars = sc.objdict(
pop_size=1e3,
pop_infected=10,
pop_type='hybrid',
n_days=90,
)
#%% Define the interventions
# 1. Dynamic pars
i00 = cv.test_prob(start_day=5, symp_prob=0.3)
i01 = 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
i02 = 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
i03 = cv.change_beta([30, 50], [0.0, 1], layers='h')
i04 = cv.change_beta([30, 40, 60], [0.0, 1.0, 0.5])
# 4. Clip edges -- should match the change_beta scenarios
i05 = cv.clip_edges(start_day=30, end_day=50, change={'h': 0.0})
i06 = cv.clip_edges(start_day=30, end_day=40, change=0.0)
i07 = cv.clip_edges(start_day=60, end_day=None, change=0.5)
# 5. Test number
i08 = cv.test_num(daily_tests=[100, 100, 100, 0, 0, 0] *
(pars.n_days // 6))
# 6. Test probability
i09 = cv.test_prob(symp_prob=0.1)
# 7. Contact tracing
i10 = 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)
i11 = 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
i12 = cv.clip_edges(start_day=18, change={'s': 0.0}) # Close schools
i13 = cv.clip_edges(start_day=20, end_day=32, change={
'w': 0.7,
'c': 0.7
}) # Reduce work and community
i14 = cv.clip_edges(start_day=32, end_day=45, change={
'w': 0.3,
'c': 0.3
}) # Reduce work and community more
i15 = cv.clip_edges(start_day=45,
end_day=None,
change={
'w': 0.9,
'c': 0.9
}) # Reopen work and community more
i16 = 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
i17 = 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
#%% Create and run the simulations
sims = sc.objdict()
sims.dynamic = cv.Sim(pars=pars, interventions=[i00, i01])
sims.sequence = cv.Sim(pars=pars, interventions=i02)
sims.change_beta1 = cv.Sim(pars=pars, interventions=i03)
sims.clip_edges1 = cv.Sim(
pars=pars, interventions=i05) # Roughly equivalent to change_beta1
sims.change_beta2 = cv.Sim(pars=pars, interventions=i04)
sims.clip_edges2 = cv.Sim(
pars=pars, interventions=[i06,
i07]) # Roughly euivalent to change_beta2
sims.test_num = cv.Sim(pars=pars, interventions=i08)
sims.test_prob = cv.Sim(pars=pars, interventions=i09)
sims.tracing = cv.Sim(pars=pars, interventions=[i10, i11])
sims.combo = cv.Sim(pars=pars,
interventions=[i12, i13, i14, i15, i16, i17])
for key, sim in sims.items():
sim.label = key
sim.run(verbose=verbose)
#%% Plotting
if do_plot:
for sim in sims.values():
print(f'Running {sim.label}...')
sim.plot()
fig = pl.gcf()
fig.axes[0].set_title(f'Simulation: {sim.label}')
return
def intervention_build_sequence(self, day_list, intervention_list):
my_sequence = cv.sequence(days=day_list,
interventions=intervention_list)
self.interventions = my_sequence
def test_interventions(do_plot=False,
do_show=True,
do_save=False,
fig_path=None):
sc.heading('Test of testing interventions')
sc.heading('Setting up...')
sc.tic()
n_runs = 3
verbose = 1
base_pars = {
'pop_size': 1000,
'pop_type': 'hybrid',
}
base_sim = cv.Sim(base_pars) # create sim object
n_people = base_sim['pop_size']
npts = base_sim.npts
# Define overall testing assumptions
# Remember that this is the daily % of the population that gets tested. S Korea (one of the highest-testing places) tested
# an average of 10000 people/day over March, or 270,000 in total. This is ~200 people per million every day (0.02%)....
max_optimistic_testing = 0.1 # ... which means that this is an artificially high number, for testing purposes only!!
optimistic_daily_tests = [
max_optimistic_testing * n_people
] * npts # Very best-case scenario for asymptomatic testing
# Define the scenarios
scenarios = {
'baseline': {
'name': 'Status quo, no testing',
'pars': {
'interventions': None,
}
},
'test_skorea': {
'name':
'Assuming South Korea testing levels of 0.02% daily (untargeted); isolate positives',
'pars': {
'interventions':
cv.test_num(daily_tests=optimistic_daily_tests)
}
},
'floating': {
'name': 'Test with constant probability based on symptoms',
'pars': {
'interventions':
cv.test_prob(symp_prob=max_optimistic_testing, asymp_prob=0.0)
}
},
'sequence': {
'name': 'Historical switching to probability',
'pars': {
'interventions':
cv.sequence(
days=[10, 51],
interventions=[
cv.test_num(daily_tests=optimistic_daily_tests),
cv.test_prob(symp_prob=0.2, asymp_prob=0.002),
])
}
},
}
metapars = {'n_runs': n_runs}
scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios)
scens.run(verbose=verbose, debug=debug)
to_plot = ['cum_infections', 'n_infectious', 'new_tests', 'new_diagnoses']
fig_args = dict(figsize=(20, 24))
if do_plot:
scens.plot(do_save=do_save,
do_show=do_show,
fig_path=fig_path,
interval=7,
fig_args=fig_args,
to_plot=to_plot)
return scens
def test_all_interventions():
''' Test all interventions supported by Covasim '''
pars = sc.objdict(
pop_size=1e3,
pop_infected=10,
pop_type='hybrid',
n_days=90,
)
#%% 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
i8a = cv.clip_edges(days=18, 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
#%% Create and run the simulations
sims = sc.objdict()
sims.dynamic = cv.Sim(pars=pars, interventions=[i1a, i1b])
sims.sequence = cv.Sim(pars=pars, interventions=i2)
sims.change_beta1 = cv.Sim(pars=pars, interventions=i3a)
sims.clip_edges1 = cv.Sim(
pars=pars, interventions=i4a) # Roughly equivalent to change_beta1
sims.change_beta2 = cv.Sim(pars=pars, interventions=i3b)
sims.clip_edges2 = cv.Sim(
pars=pars, interventions=i4b) # Roughly equivalent to change_beta2
sims.test_num = cv.Sim(pars=pars, interventions=i5)
sims.test_prob = cv.Sim(pars=pars, interventions=i6)
sims.tracing = cv.Sim(pars=pars, interventions=[i7a, i7b])
sims.combo = cv.Sim(pars=pars, interventions=[i8a, i8b, i8c, i8d])
for key, sim in sims.items():
sim.label = key
sim.run(verbose=verbose)
#%% Plotting
if do_plot:
for sim in sims.values():
print(f'Running {sim.label}...')
sim.plot()
fig = pl.gcf()
fig.axes[0].set_title(f'Simulation: {sim.label}')
return
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
i00 = cv.test_prob(start_day=5, symp_prob=0.3)
i01 = cv.dynamic_pars({
'beta': {
'days': [40, 50],
'vals': [0.005, 0.015]
},
'diag_factor': {
'days': 30,
'vals': 0.0
}
})
# 2. Sequence
i02 = cv.sequence(days=[20, 40, 60],
interventions=[
cv.test_num(daily_tests=[20] * n_days),
cv.test_prob(symp_prob=0.0),
cv.test_prob(symp_prob=0.2),
])
# 3. Change beta
i03 = cv.change_beta([30, 50], [0.0, 1], layers='h')
i04 = cv.change_beta([30, 40, 60], [0.0, 1.0, 0.5])
# 4. Clip edges -- should match the change_beta scenarios
i05 = cv.clip_edges(days=[30, 50], changes={'h': 0.0})
i06 = cv.clip_edges(days=[30, 40], changes=0.0)
i07 = cv.clip_edges(days=[60, None], changes=0.5)
# 5. Test number
i08 = cv.test_num(daily_tests=[100, 100, 100, 0, 0, 0] * (n_days // 6))
def test_interventions(do_plot=False,
do_show=True,
do_save=False,
fig_path=None):
sc.heading('Test of testing interventions')
sc.heading('Setting up...')
sc.tic()
n_runs = 3
verbose = 1
base_pars = {
'pop_size': 1000,
'use_layers': True,
}
base_sim = cv.Sim(base_pars) # create sim object
n_people = base_sim['pop_size']
npts = base_sim.npts
# Define overall testing assumptions
# As the most optimistic case, we assume countries could get to South Korea's testing levels. S Korea has tested
# an average of 10000 people/day over March, or 270,000 in total. This is ~200 people per million every day (0.02%).
max_optimistic_testing = 0.0002
optimistic_daily_tests = [
max_optimistic_testing * n_people
] * npts # Very best-case scenario for asymptomatic testing
# Define the scenarios
scenarios = {
'baseline': {
'name': 'Status quo, no testing',
'pars': {
'interventions': None,
}
},
'test_skorea': {
'name':
'Assuming South Korea testing levels of 0.02% daily (untargeted); isolate positives',
'pars': {
'interventions':
cv.test_num(daily_tests=optimistic_daily_tests)
}
},
'tracing': {
'name':
'Assuming South Korea testing levels of 0.02% daily (with contact tracing); isolate positives',
'pars': {
'interventions': [
cv.test_num(daily_tests=optimistic_daily_tests),
cv.dynamic_pars({
'quar_eff': {
'days': 20,
'vals': [{
'h': 0.1,
's': 0.1,
'w': 0.1,
'c': 0.1
}]
}
})
] # This means that people who've been in contact with known positives isolate with 90% effectiveness
}
},
'floating': {
'name': 'Test with constant probability based on symptoms',
'pars': {
'interventions':
cv.test_prob(symp_prob=max_optimistic_testing, asymp_prob=0.0)
}
},
# 'historical': {
# 'name': 'Test a known number of positive cases',
# 'pars': {
# 'interventions': cv.test_historical(n_tests=[100]*npts, n_positive = [1]*npts)
# }
# },
'sequence': {
'name': 'Historical switching to probability',
'pars': {
'interventions':
cv.sequence(days=[10, 51],
interventions=[
cv.test_num(daily_tests=[1000] * npts),
cv.test_prob(symp_prob=0.2, asymp_prob=0.002),
])
}
},
}
metapars = {'n_runs': n_runs}
scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios)
scens.run(verbose=verbose, debug=debug)
if do_plot:
scens.plot(do_save=do_save, do_show=do_show, fig_path=fig_path)
return scens
def modify_sim(sim, scenpars, label=None, runinfo=None):
''' Modify the simulation for the scenarios '''
print(
f' Note: modifying simulation {label} at day={sim.t}, date={sim.date(sim.t)}, scenpars:\n{scenpars}'
)
sim.sceninfo.scen_start = '2020-06-01'
sim.sceninfo.scale_down = '2020-07-01'
last_calib_day = sim.day(sim.sceninfo.calib_end)
first_scen_day = sim.day(sim.sceninfo.scen_start)
scale_down_day = sim.day(sim.sceninfo.scale_down)
n_days = 5
scale_range = np.arange(n_days)
ramp_up = np.linspace(0, 1, n_days)
ramp_down = 1 - ramp_up
for ilabel in ['clip_w', 'clip_c']:
interv = sim.get_interventions(ilabel)
valid_days = sc.findinds(interv.days <= last_calib_day)
# Do reopening: modify clip_edges
v0 = interv.changes[valid_days[-1]]
v1 = scenpars['reopen'][0]
v2 = scenpars['reopen'][1]
scale_up = ramp_down * v0 + ramp_up * v1
scale_down = ramp_down * v1 + ramp_up * v2
scale_days = np.append(scale_range + first_scen_day,
scale_range + scale_down_day)
scale_vals = np.append(scale_up, scale_down)
interv.days = np.append(interv.days[valid_days], scale_days)
interv.changes = np.append(interv.changes[valid_days], scale_vals)
# Change iso_factor and quar_factor
sim.pars['iso_factor'] = sc.dcp(scenpars['i_factor'])
sim.pars['quar_factor'] = sc.dcp(scenpars['q_factor'])
# Implement testing & tracing interventions
ctpars = {k: scenpars[k] for k in ['trace_probs', 'trace_time']}
tn = sim.get_interventions('tn')
if scenpars['which'] == 'actual':
tn.test_delay = scenpars.test_delay
sim['interventions'] += [
cv.contact_tracing(start_day=first_scen_day,
label='contact_tracing',
**ctpars)
]
elif scenpars['which'] == 'low':
offset = 7 # So it's constant
tn.daily_tests[first_scen_day - tn.start_day -
offset:] = scenpars['n_tests']
sim['interventions'] += [
cv.contact_tracing(start_day=first_scen_day,
label='contact_tracing',
**ctpars)
]
elif scenpars['which'] == 'high':
cv.set_seed(
sim['rand_seed']) # Just in case since we use a random number
tn.end_day = last_calib_day # End the test_num intervention
n_weeks = 4 # Ramp up the test_prob and contact_tracing interventions
days = first_scen_day + np.arange(n_weeks) * 7
tpramp = np.maximum(
0,
np.linspace(0.2, 0.7, n_weeks + 1)[1:] +
np.random.randn(n_weeks) * 0.1
) # Don't start from 0 and don't make it a perfectly smooth ramp
ctramp = np.linspace(0.0, 0.7, n_weeks + 1)[1:] # Don't start from 0
tppars = {
k: scenpars[k]
for k in [
'symp_prob', 'asymp_prob', 'symp_quar_prob', 'asymp_quar_prob',
'test_delay'
]
}
tplist = []
ctlist = []
for w in range(n_weeks):
tp = cv.test_prob(quar_policy=[0], **tppars, label=f'tp_{w}')
ct = cv.contact_tracing(label=f'ct_{w}', **ctpars)
tp.symp_prob *= tpramp[w]
tp.asymp_prob *= tpramp[w]
ct.trace_probs = sc.dcp(ct.trace_probs)
for key in ct.trace_probs.keys():
ct.trace_probs[key] *= ctramp[w]
tplist.append(tp)
ctlist.append(ct)
print(f'I AM {w} {ct.trace_probs}')
tpseq = cv.sequence(days=days, interventions=tplist)
ctseq = cv.sequence(days=days, interventions=ctlist)
tpseq.initialize(sim)
ctseq.initialize(sim)
sim['interventions'] += [tpseq, ctseq]
# Final tidying
sim.label = label
sim.runinfo = runinfo
sim.sceninfo.scenpars = scenpars
return sim