def test_calibration():
sc.heading('Testing calibration')
pars = dict(
verbose=0,
start_day='2020-02-05',
pop_size=1e3,
pop_scale=4,
interventions=[cv.test_prob(symp_prob=0.1)],
)
sim = cv.Sim(pars, datafile='example_data.csv')
calib_pars = dict(beta=[0.013, 0.005, 0.020], test_prob=[0.01, 0.00, 0.30])
def set_test_prob(sim, calib_pars):
tp = sim.get_intervention(cv.test_prob)
tp.symp_prob = calib_pars['test_prob']
return sim
calib = sim.calibrate(calib_pars=calib_pars,
custom_fn=set_test_prob,
n_trials=5)
calib.plot(to_plot=['cum_deaths', 'cum_diagnoses'])
assert calib.after.fit.mismatch < calib.before.fit.mismatch
return calib
def make_sim(use_defaults=False, do_plot=False):
'''
Define a default simulation for testing the baseline -- use hybrid and include
interventions to increase coverage. If run directly (not via pytest), also
plot the sim by default.
'''
# Define the parameters
intervs = [
cv.change_beta(days=40, changes=0.5),
cv.test_prob(start_day=20, symp_prob=0.1, asymp_prob=0.01)
] # Common interventions
pars = dict(
pop_size=20000, # Population size
pop_infected=
100, # Number of initial infections -- use more for increased robustness
pop_type=
'hybrid', # Population to use -- "hybrid" is random with household, school,and work structure
verbose=0, # Don't print details of the run
interventions=intervs # Include the most common interventions
)
# Create the sim
if use_defaults:
sim = cv.Sim()
else:
sim = cv.Sim(pars)
# Optionally plot
if do_plot:
s2 = sim.copy()
s2.run()
s2.plot()
return sim
def parse_interventions(int_pars):
'''
Parse interventions. Format
Args:
int_pars = {
'social_distance': [
{'start': 1, 'end': 19, 'level': 'aggressive'},
{'start': 20, 'end': 30, 'level': 'mild'},
],
'school_closures': [
{'start': 12, 'end': 14}
],
'symptomatic_testing': [
{'start': 8, 'end': 25, 'level': 60}
]}
'''
intervs = []
if int_pars is not None:
masterlist = []
for ikey,intervlist in int_pars.items():
for iconfig in intervlist:
iconfig['ikey'] = ikey
masterlist.append(dict(iconfig))
for iconfig in masterlist:
ikey = iconfig['ikey']
start = iconfig['start']
end = iconfig['end']
if ikey == 'social_distance':
level = iconfig['level']
mapping = {
'mild': 0.8,
'moderate': 0.5,
'aggressive': 0.2,
}
change = mapping[level]
interv = cv.change_beta(days=[start, end], changes=[change, 1.0])
elif ikey == 'school_closures':
change = 0.7
interv = cv.change_beta(days=[start, end], changes=[change, 1.0], layers='a')
elif ikey == 'symptomatic_testing':
level = iconfig['level']
level = float(level)/100
asymp_prob = 0.0
delay = 0.0
interv = cv.test_prob(start_day=start, end_day=end, symp_prob=level, asymp_prob=asymp_prob, test_delay=delay)
elif ikey == 'contact_tracing':
trace_prob = {'a':1.0}
trace_time = {'a':0.0}
interv = cv.contact_tracing(start_day=start, end_day=end, trace_probs=trace_prob, trace_time=trace_time)
else:
raise NotImplementedError
intervs.append(interv)
return intervs
def test_data_interventions():
sc.heading('Testing data interventions and other special cases')
# Create sim
sim = cv.Sim(pop_size=100, n_days=60, datafile=csv_file, verbose=verbose)
# Intervention conversion
ce = cv.InterventionDict(**{
'which': 'clip_edges',
'pars': {
'days': [10, 30],
'changes': [0.5, 1.0]
}
})
print(ce)
with pytest.raises(sc.KeyNotFoundError):
cv.InterventionDict(**{
'which': 'invalid',
'pars': {
'days': 10,
'changes': 0.5
}
})
# Test numbers and contact tracing
swab_dict = {'dist': 'uniform', 'par1': 1, 'par2': 3}
tp1 = cv.test_prob(0.05,
start_day=5,
end_day=15,
ili_prev=0.1,
swab_delay=swab_dict,
subtarget={
'inds': lambda sim: cv.true(sim.people.age > 50),
'vals': 0.3
})
tn1 = cv.test_num(10,
start_day=3,
end_day=20,
ili_prev=0.1,
swab_delay=swab_dict)
tn2 = cv.test_num(daily_tests='data',
quar_policy=[0, 5],
subtarget={
'inds': lambda sim: cv.true(sim.people.age > 50),
'vals': 1.2
})
ct = cv.contact_tracing(presumptive=True, capacity=5)
# Create and run
sim['interventions'] = [ce, tp1, tn1, tn2, ct]
sim.run()
return
def intervention_set_test_prob(self, symptomatic_prob=0, asymptomatic_prob=0,
asymptomatic_quarantine_prob=0, symp_quar_prob=0,
test_sensitivity=1.0, loss_prob=0.0, test_delay=1,
start_day=0):
self.interventions = test_prob(symp_prob=symptomatic_prob,
asymp_prob=asymptomatic_prob,
asymp_quar_prob=asymptomatic_quarantine_prob,
symp_quar_prob=symp_quar_prob,
test_sensitivity=test_sensitivity,
loss_prob=loss_prob,
test_delay=test_delay,
start_day=start_day)
pass
def test_basic_contact_trace():
tweaking = {
'n_days': 60,
'pop_size': 2000,
'pop_type': 'hybrid'
}
start_days = []
interventions = []
test_prob_start_day = 35
prob_test_perfect_symptomatic = test_prob(symp_prob=1.0, symp_quar_prob=1.0, asymp_quar_prob=1.0,
test_sensitivity=1.0, test_delay=1, start_day=test_prob_start_day)
start_days.append(test_prob_start_day)
interventions.append(prob_test_perfect_symptomatic)
contact_trace_start_day = 35
all_contacts = {
'h': 1.0,
'w': 1.0,
's': 1.0,
'c': 1.0
}
single_day_delays = {
'h': 1,
'w': 1,
's': 1,
'c': 1
}
brutal_contact_trace = contact_tracing(trace_probs=all_contacts,
trace_time=single_day_delays,
start_day=contact_trace_start_day)
start_days.append(contact_trace_start_day)
interventions.append(brutal_contact_trace)
tweaking['interventions'] = interventions
test_stuff_simulation = Sim(pars=tweaking)
test_stuff_simulation.run()
test_stuff_results = test_stuff_simulation.to_json(tostring=False)
with open("DEBUG_test_intervention_list_simulation.json","w") as outfile:
json.dump(test_stuff_results, outfile, indent=4, sort_keys=True)
pass
assert test_stuff_results["results"]["n_symptomatic"][test_prob_start_day] > 0 # If there are symptomatics
assert sum(test_stuff_results["results"]["new_tests"][test_prob_start_day:test_prob_start_day + 5]) > 0 # then there should be tests
assert sum(test_stuff_results["results"]["new_diagnoses"][test_prob_start_day + 1:test_prob_start_day + 6]) > 0 # and some diagnoses
assert sum(test_stuff_results["results"]["new_diagnoses"][test_prob_start_day + 2:test_prob_start_day + 7]) > 0 # and therefore some quarantined
pass
def test_fit():
sc.heading('Testing fitting function')
# Create a testing intervention to ensure some fit to data
tp = cv.test_prob(0.1)
sim = cv.Sim(pars,
rand_seed=1,
interventions=tp,
datafile="example_data.csv")
sim.run()
# Checking that Fit can handle custom input
custom_inputs = {
'custom_data': {
'data': np.array([1, 2, 3]),
'sim': np.array([1, 2, 4]),
'weights': [2.0, 3.0, 4.0]
}
}
fit1 = sim.compute_fit(custom=custom_inputs, compute=True)
# Test that different seed will change compute results
sim2 = cv.Sim(pars,
rand_seed=2,
interventions=tp,
datafile="example_data.csv")
sim2.run()
fit2 = sim2.compute_fit(custom=custom_inputs)
assert fit1.mismatch != fit2.mismatch, "Differences between fit and data remains unchanged after changing sim seed"
# Test custom analyzers
actual = np.array([1, 2, 4])
predicted = np.array([1, 2, 3])
def simple(actual, predicted, scale=2):
return np.sum(abs(actual - predicted)) * scale
gof1 = cv.compute_gof(actual, predicted, normalize=False, as_scalar='sum')
gof2 = cv.compute_gof(actual, predicted, estimator=simple, scale=1.0)
assert gof1 == gof2
with pytest.raises(Exception):
cv.compute_gof(actual, predicted, skestimator='not an estimator')
with pytest.raises(Exception):
cv.compute_gof(actual, predicted, estimator='not an estimator')
if do_plot:
fit1.plot()
return fit1
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}'
)
# Do reopening: modify clip_edges
last_calib_day = sim.day(sim.sceninfo.calib_end)
first_scen_day = sim.day(sim.sceninfo.scen_start)
for ilabel in ['clip_w', 'clip_c']:
interv = get_interv(sim, ilabel)
valid_days = sc.findinds(interv.days <= last_calib_day)
interv.days = np.append(
interv.days[valid_days], first_scen_day
) # NB, repr of intervention will be wrong with direct modification!
interv.changes = np.append(interv.changes[valid_days],
scenpars['reopen'])
# 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
tppars = {
k: scenpars[k]
for k in [
'symp_prob', 'asymp_prob', 'symp_quar_prob', 'asymp_quar_prob',
'test_delay'
]
}
ctpars = {k: scenpars[k] for k in ['trace_probs', 'trace_time']}
tp = cv.test_prob(start_day=first_scen_day, quar_policy=[0], **tppars)
ct = cv.contact_tracing(start_day=first_scen_day,
label='contact_tracing',
**ctpars)
sim['interventions'] += [tp, ct]
# Final tidying
sim.label = label
sim.runinfo = runinfo
sim.sceninfo.scenpars = scenpars
return sim
def make_sim(use_defaults=False, do_plot=False, **kwargs):
'''
Define a default simulation for testing the baseline -- use hybrid and include
interventions to increase coverage. If run directly (not via pytest), also
plot the sim by default.
'''
# Define the interventions
tp = cv.test_prob(start_day=20, symp_prob=0.1, asymp_prob=0.01)
vx = cv.vaccinate_prob('pfizer', days=30, prob=0.1)
cb = cv.change_beta(days=40, changes=0.5)
ct = cv.contact_tracing(trace_probs=0.3, start_day=50)
# Define the parameters
pars = dict(
use_waning=True, # Whether or not to use waning and NAb calculations
pop_size=20e3, # Population size
pop_infected=
100, # Number of initial infections -- use more for increased robustness
pop_type=
'hybrid', # Population to use -- "hybrid" is random with household, school,and work structure
n_days=60, # Number of days to simulate
verbose=0, # Don't print details of the run
rand_seed=2, # Set a non-default seed
interventions=[cb, tp, ct,
vx], # Include the most common interventions
)
pars = sc.mergedicts(pars, kwargs)
# Create the sim
if use_defaults:
sim = cv.Sim()
else:
sim = cv.Sim(pars)
# Optionally plot
if do_plot:
s2 = sim.copy()
s2.run()
s2.plot()
return sim
def test_fit():
sc.heading('Testing fitting function')
# Create a testing intervention to ensure some fit to data
tp = cv.test_prob(0.1)
sim = cv.Sim(pars, rand_seed=1, interventions=tp, datafile="example_data.csv")
sim.run()
# Checking that Fit can handle custom input
custom_inputs = {'custom_data':{'data':np.array([1,2,3]), 'sim':np.array([1,2,4]), 'weights':[2.0, 3.0, 4.0]}}
fit1 = sim.compute_fit(custom=custom_inputs, compute=True)
# Test that different seed will change compute results
sim2 = cv.Sim(pars, rand_seed=2, interventions=tp, datafile="example_data.csv")
sim2.run()
fit2 = sim2.compute_fit(custom=custom_inputs)
assert fit1.mismatch != fit2.mismatch, f"Differences between fit and data remains unchanged after changing sim seed"
return fit1
cv.test_num(start_day=start_day,
daily_tests=[0.005 * n_people] * n_days,
subtarget={
'inds': sim.people.age > 50,
'val': 2.0
}),
}
},
'testelderly': {
'name': 'Test the elderly',
'pars': {
'interventions':
cv.test_prob(start_day=start_day,
symp_prob=0.1,
asymp_prob=0.005,
subtarget={
'inds': sim.people.age > 50,
'val': 2.0
}),
}
},
}
to_plot = [
'cum_infections',
'new_infections',
'cum_diagnoses',
'n_severe',
'n_critical',
'cum_deaths',
]
s_prob_july = 0.0171
s_prob_august = 0.0171
t_delay = 1.0
iso_vals = [{k:0.1 for k in 'hswc'}]
#tracing level at 42.35% in June; 47.22% in July
t_eff_june = 0.42
t_eff_july = 0.47
t_probs_june = {k:t_eff_june for k in 'hwsc'}
t_probs_july = {k:t_eff_july for k in 'hwsc'}
trace_d_1 = {'h':0, 's':1, 'w':1, 'c':2}
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.009, asymp_prob=0.0, symp_quar_prob=0.0, asymp_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, asymp_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=0.00075, symp_quar_prob=0.0, asymp_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=0.00075, symp_quar_prob=0.0, asymp_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=0.00075, symp_quar_prob=0.0, asymp_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=0.00075, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tti_day_august, test_delay=t_delay),
cv.dynamic_pars({'iso_factor': {'days': te_day, 'vals': iso_vals}}),
cv.contact_tracing(trace_probs=t_probs_june, trace_time=trace_d_1, start_day=tti_day, end_day=tti_day_july-1),
cv.contact_tracing(trace_probs=t_probs_july, trace_time=trace_d_1, start_day=tti_day_july),
]
elif tti_scen == 'optimal_TTI':
# Tracing and enhanced testing strategy of symptimatics from 1st June
#testing in August needs to increase to avoid a second wave
s_prob_march = 0.009
def make_sim(seed,
beta,
calibration=True,
scenario=None,
delta_beta=1.6,
future_symp_test=None,
end_day=None,
verbose=0):
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 1500
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 = '2021-03-31'
pars = sc.objdict(
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_severe_prob=0.4,
rel_crit_prob=2.3,
#rel_death_prob=1.5,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1.0 # 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, 0.50, 0.70],
'2020-12-25': [1.50, 0.00, 0.20, 0.90],
'2020-12-26': [1.50, 0.00, 0.20, 0.90],
'2020-12-31': [1.50, 0.00, 0.20, 0.90],
'2021-01-01': [1.50, 0.00, 0.20, 0.90],
'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],
'2021-01-18': [1.25, 0.14, 0.30, 0.40]
})
if not calibration:
##no schools until 1st 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)
if scenario == 'FNL':
beta_s_feb22, beta_s_mar01, beta_s_mar08, beta_s_mar15, beta_s_mar22, beta_s_mar29, beta_s_apr01 = 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.02
##primaries and yars 11 and 13 back on 22/02 all other years 01/03
##9/14 years back -30% transmission reduction = 45% reduction remaining from 22/02
##transmision increases to 63% remaining from 01/03
##Easter holiday 01/04-08/04
elif scenario == 'staggeredPNL':
beta_s_feb22, beta_s_mar01, beta_s_mar08, beta_s_mar15, beta_s_mar22, beta_s_mar29, beta_s_apr01 = 0.14, 0.14, 0.40, sbv, sbv, sbv, 0.02,
##primaries and secondaries back fully 22/02; 14/14 years but assume 90% attendence and
##30% reduction in transmission due to hygiene, masks etc to remaining transmision to 0.63
##Easter holiday 01/04-08/04
elif scenario == 'fullPNL':
beta_s_feb22, beta_s_mar01, beta_s_mar08, beta_s_mar15, beta_s_mar22, beta_s_mar29, beta_s_apr01 = 0.14, 0.14, sbv, sbv, sbv, sbv, 0.02
elif scenario == 'primaryPNL':
beta_s_feb22, beta_s_mar01, beta_s_mar08, beta_s_mar15, beta_s_mar22, beta_s_mar29, beta_s_apr01 = 0.14, 0.14, 0.31, 0.31, 0.40, 0.40, 0.02
elif scenario == 'rotasecondaryPNL':
beta_s_feb22, beta_s_mar01, beta_s_mar08, beta_s_mar15, beta_s_mar22, beta_s_mar29, beta_s_apr01 = 0.14, 0.14, 0.31, 0.31, sbv, sbv, 0.02
beta_scens = sc.odict({
'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, beta_s_feb22, 0.30, 0.40],
'2021-03-01': [1.25, beta_s_mar01, 0.30, 0.40],
'2021-03-08': [1.25, beta_s_mar08, 0.30, 0.50],
'2021-03-15': [1.25, beta_s_mar15, 0.30, 0.50],
'2021-03-22': [1.25, beta_s_mar22, 0.30, 0.50],
'2021-03-29': [1.25, beta_s_mar29, 0.30, 0.50],
'2021-04-01': [1.25, beta_s_apr01, 0.30, 0.50],
'2021-04-12': [1.25, 0.02, 0.30, 0.50],
'2021-04-19': [1.25, sbv, 0.50, 0.70],
'2021-04-26': [1.25, sbv, 0.50, 0.70],
'2021-05-03': [1.25, sbv, 0.50, 0.70]
})
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 a new change in beta to represent the takeover of the novel variant VOC 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
voc_days = np.linspace(sim.day('2020-08-01'), sim.day('2021-01-30'), 31)
voc_prop = 0.6 / (
1 + np.exp(-0.075 * (voc_days - sim.day('2020-09-30')))
) # Use a logistic growth function to approximate fig 2A of https://cmmid.github.io/topics/covid19/uk-novel-variant.html
voc_change = voc_prop * 1.63 + (1 - voc_prop) * 1.
voc_beta = cv.change_beta(days=voc_days, changes=voc_change)
interventions = [h_beta, w_beta, s_beta, c_beta, voc_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_vac = sim.day('2020-12-20')
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_sept = 0.08769
s_prob_oct = 0.08769
s_prob_nov = 0.08769
s_prob_may = 0.02769
s_prob_june = 0.02769
s_prob_july = 0.02769
s_prob_august = 0.03769
s_prob_sep = 0.08769
s_prob_oct = 0.08769
s_prob_nov = 0.08769
s_prob_dec = 0.08769
if future_symp_test is None: future_symp_test = s_prob_dec
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: 0.5 for k in 'hswc'}]
#isolation october
iso_vals3 = [{k: 0.5 for k in 'hswc'}]
#isolation november
iso_vals4 = [{k: 0.5 for k in 'hswc'}]
#isolation december
iso_vals5 = [{k: 0.5 for k in 'hswc'}]
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.0075,
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=future_symp_test,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_jan,
test_delay=t_delay),
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='2020-06-01',
quar_period=10),
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])]
# vaccination interventions
interventions += [
utils.two_dose_daily_delayed(200e3,
start_day=tti_day_vac,
dose_delay=14,
delay=10 * 7,
take_prob=1.0,
rel_symp=0.05,
rel_trans=0.9,
cumulative=[0.7, 1.0],
dose_priority=[1, 0.1])
]
analyzers = []
analyzers += [
utils.record_dose_flows(vacc_class=utils.two_dose_daily_delayed)
]
# 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,
change=0.42,
policy='remain',
threshold=5,
symp_prob=0.01,
end_day=None):
start_day = '2020-06-15'
if end_day is None: end_day = '2021-04-30'
total_pop = 11.9e6 # Population of central Vietnam
n_agents = 100e3
pop_scale = total_pop / n_agents
# Calibration parameters
pars = {
'pop_size': n_agents,
'pop_infected': 0,
'pop_scale': pop_scale,
'rand_seed': seed,
'beta': beta,
'start_day': start_day,
'end_day': end_day,
'verbose': 0,
'rescale': True,
'iso_factor':
dict(h=0.5, s=0.01, w=0.01,
c=0.1), # Multiply beta by this factor for people in isolation
'quar_factor':
dict(h=1.0, s=0.2, w=0.2,
c=0.2), # Multiply beta by this factor for people in quarantine
'location': 'vietnam',
'pop_type': 'hybrid',
'age_imports': [50, 80],
'rel_crit_prob':
1.75, # Calibration parameter due to hospital outbreak
'rel_death_prob': 2., # Calibration parameter due to hospital outbreak
}
# Make a sim without parameters, just to load in the data to use in the testing intervention and to get the sim days
sim = cv.Sim(start_day=start_day, datafile="vietnam_data.csv")
# Set up import assumptions
pars['dur_imports'] = sc.dcp(sim.pars['dur'])
pars['dur_imports']['exp2inf'] = {
'dist': 'lognormal_int',
'par1': 0.0,
'par2': 0.0
}
pars['dur_imports']['inf2sym'] = {
'dist': 'lognormal_int',
'par1': 0.0,
'par2': 0.0
}
pars['dur_imports']['sym2sev'] = {
'dist': 'lognormal_int',
'par1': 0.0,
'par2': 2.0
}
pars['dur_imports']['sev2crit'] = {
'dist': 'lognormal_int',
'par1': 1.0,
'par2': 3.0
}
pars['dur_imports']['crit2die'] = {
'dist': 'lognormal_int',
'par1': 3.0,
'par2': 3.0
}
# Define import array
import_end = sim.day('2020-07-15')
border_start = sim.day('2020-11-30') # Open borders for one month
border_end = sim.day('2020-12-31') # Then close them again
final_day_ind = sim.day('2021-04-30')
imports = np.concatenate((
np.array([
1, 0, 0, 0, 2, 2, 8, 4, 1, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 2, 3, 1, 1, 3, 0, 3, 0, 1, 6, 1, 5, 0, 0
]), # Generated from cv.n_neg_binomial(1, 0.25) but then hard-copied to remove variation when calibrating
pl.zeros(
border_start - import_end
), # No imports from the end of the 1st importation window to the border reopening
cv.n_neg_binomial(
1, 0.25, border_end - border_start
), # Negative-binomial distributed importations each day
pl.zeros(final_day_ind - border_end)))
pars['n_imports'] = imports
# Add testing and tracing interventions
trace_probs = {'h': 1, 's': 0.95, 'w': 0.8, 'c': 0.05}
trace_time = {'h': 0, 's': 2, 'w': 2, 'c': 5}
pars['interventions'] = [
# Testing and tracing
cv.test_num(daily_tests=sim.data['new_tests'].rolling(3).mean(),
start_day=2,
end_day=sim.day('2020-08-22'),
symp_test=80,
quar_test=80,
do_plot=False),
cv.test_prob(start_day=sim.day('2020-08-23'),
end_day=sim.day('2020-11-30'),
symp_prob=0.05,
asymp_quar_prob=0.5,
do_plot=False),
cv.test_prob(start_day=sim.day('2020-12-01'),
symp_prob=symp_prob,
asymp_quar_prob=0.5,
trigger=cv.trigger('date_diagnosed', 5),
triggered_vals={'symp_prob': 0.2},
do_plot=False),
cv.contact_tracing(start_day=0,
trace_probs=trace_probs,
trace_time=trace_time,
do_plot=False),
# Change death and critical probabilities
cv.dynamic_pars(
{
'rel_death_prob': {
'days': sim.day('2020-08-31'),
'vals': 1.0
},
'rel_crit_prob': {
'days': sim.day('2020-08-31'),
'vals': 1.0
}
},
do_plot=False
), # Assume these were elevated due to the hospital outbreak but then would return to normal
# Increase precautions (especially mask usage) following the outbreak, which are then abandoned after 40 weeks of low case counts
cv.change_beta(days=0,
changes=change,
trigger=cv.trigger('date_diagnosed', 5)),
# Close schools and workplaces
cv.clip_edges(days=['2020-07-28', '2020-09-14'],
changes=[0.1, 1.],
layers=['s'],
do_plot=True),
cv.clip_edges(days=['2020-07-28', '2020-09-05'],
changes=[0.1, 1.],
layers=['w'],
do_plot=False),
# Dynamically close them again if cases go over the threshold
cv.clip_edges(days=[170],
changes=[0.1],
layers=['s'],
trigger=cv.trigger('date_diagnosed', threshold)),
cv.clip_edges(days=[170],
changes=[0.1],
layers=['w'],
trigger=cv.trigger('date_diagnosed', threshold)),
]
if policy != 'remain':
pars['interventions'] += [
cv.change_beta(days=160,
changes=1.0,
trigger=cv.trigger('date_diagnosed',
2,
direction='below',
smoothing=28))
]
if policy == 'dynamic':
pars['interventions'] += [
cv.change_beta(days=170,
changes=change,
trigger=cv.trigger('date_diagnosed', threshold)),
]
sim = cv.Sim(pars=pars, datafile="vietnam_data.csv")
sim.initialize()
return sim
p3 = dict(
pop_size = 20000,
pop_infected = 50,
pop_scale = 10,
rescale = False,
)
tnp = dict(start_day=0, daily_tests=1000)
tpp = dict(symp_prob=0.1, asymp_prob=0.01)
sims = []
for st in [1000]:
tn = cv.test_num(**tnp, symp_test=st)
tp = cv.test_prob(**tpp)
ti = tn # Switch testing interventions here
s1 = cv.Sim(pars, **p1, interventions=sc.dcp(ti), label=f'full, symp_test={st}')
s2 = cv.Sim(pars, **p2, interventions=sc.dcp(ti), label=f'dynamic, symp_test={st}')
s3 = cv.Sim(pars, **p3, interventions=sc.dcp(ti), label=f'static, symp_test={st}')
sims.extend([s1, s2, s3])
msim = cv.MultiSim(sims)
sc.tic()
msim.run()
sc.toc()
for sim in msim.sims:
sim.results['n_quarantined'][:] = sim.rescale_vec
sim.results['n_quarantined'].name = 'Rescale vec'
sim.results['new_quarantined'] = sim.results['rel_test_yield']
#Testing and Isolation interventions until 1st June
#s_prob=% of sympomatic that are tested only as part of TI strategies; we fit s_prob_may to data
s_prob_march = 0.007
s_prob_april = 0.009
s_prob_may = 0.0135
a_prob = 0.0
q_prob = 0.0
t_delay = 1.0
iso_vals = [{k: 0.1 for k in 'hswc'}]
interventions += [
cv.test_prob(symp_prob=s_prob_march,
asymp_prob=0.00030,
symp_quar_prob=q_prob,
asymp_quar_prob=q_prob,
start_day=tc_day,
end_day=te_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_april,
asymp_prob=0.00050,
symp_quar_prob=q_prob,
asymp_quar_prob=q_prob,
start_day=te_day,
end_day=tt_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_may,
asymp_prob=0.00075,
symp_quar_prob=q_prob,
asymp_quar_prob=q_prob,
start_day=tt_day,
def make_sim(seed,
beta,
calibration=True,
scenario=None,
future_symp_test=None,
end_day=None,
verbose=0):
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 1500
beta = beta
asymp_factor = 2
contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20}
if end_day is None: end_day = '2021-03-31'
pars = sc.objdict(
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_severe_prob=0.5,
rel_crit_prob=2,
# rel_death_prob=1.5,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1.0 # ages 20-30
sim['prognoses']['sus_ORs'][1] = 1.0 # ages 20-30
# ADD BETA INTERVENTIONS
sbv = 0.77
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': [
1.00,
sbv,
0.60,
0.90,
],
'2020-10-26': [
1.25,
0.00,
0.60,
0.90,
],
'2020-11-01': [
1.25,
sbv,
0.20,
0.40,
],
'2020-12-03': [
1.25,
sbv,
0.60,
0.90,
],
'2020-12-20': [
1.25,
0.00,
0.40,
0.90,
],
'2020-12-25': [
1.50,
0.00,
0.30,
0.90,
],
})
if not calibration:
if scenario == 'FNL':
beta_s_jan4, beta_s_jan11, beta_s_jan18 = 0.02, 0.02, 0.02
elif scenario == 'primaryPNL':
beta_s_jan4, beta_s_jan11, beta_s_jan18 = sbv / 2, sbv / 2, sbv
elif scenario == 'staggeredPNL':
beta_s_jan4, beta_s_jan11, beta_s_jan18 = sbv / 2, 0.45, sbv
beta_scens = sc.odict({
'2021-01-04': [1.00, beta_s_jan4, 0.20, 0.30],
'2021-01-11': [1.00, beta_s_jan11, 0.20, 0.30],
'2021-01-18': [1.00, beta_s_jan18, 0.20, 0.30],
'2021-02-08': [1.00, sbv, 0.50, 0.70],
'2021-02-15': [1.00, 0.00, 0.50, 0.70],
'2021-02-21': [1.00, 0.00, 0.50, 0.70],
'2021-03-01': [1.00, sbv, 0.50, 0.70],
'2021-03-20': [1.00, sbv, 0.50, 0.70],
'2021-04-01': [1.00, sbv, 0.50, 0.70],
'2021-04-17': [1.00, 0.00, 0.50, 0.70]
})
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 a new change in beta to represent the takeover of the novel variant VOC 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
voc_days = np.linspace(sim.day('2020-09-01'), sim.day('2021-01-30'), 31)
voc_prop = 1 / (
1 + np.exp(-.1 * (voc_days - sim.day('2020-11-16')))
) # Use a logistic growth function to approximate fig 2A of https://cmmid.github.io/topics/covid19/uk-novel-variant.html
voc_change = voc_prop * 1.6 + (1 - voc_prop) * 1.
voc_beta = cv.change_beta(days=voc_days, changes=voc_change)
interventions = [h_beta, w_beta, s_beta, c_beta, voc_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')
s_prob_april = 0.008
s_prob_may = 0.03
s_prob_june = 0.01
s_prob_july = 0.01
s_prob_august = 0.01
tn = 0.09
s_prob_sep = 0.06
s_prob_oct = 0.07
s_prob_nov = 0.09
s_prob_dec = 0.1
if future_symp_test is None: future_symp_test = s_prob_dec
t_delay = 1.0
#isolation may-july
iso_vals = [{k: 0.1 for k in 'hswc'}]
#isolation august-dec
iso_vals1 = [{k: 0.7 for k in 'hswc'}]
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.0075,
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=0.00075,
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=0.00075,
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=0.00075,
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=0.00075,
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=0.00075,
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=0.00075,
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=0.00075,
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=0.00075,
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=future_symp_test,
asymp_prob=0.00075,
symp_quar_prob=0.0,
start_day=tti_day_jan,
test_delay=t_delay),
cv.contact_tracing(trace_probs={
'h': 1,
's': 0.5,
'w': 0.5,
'c': 0.05
},
trace_time={
'h': 0,
's': 1,
'w': 2,
'c': 7
},
start_day='2020-06-01',
quar_period=10),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.dynamic_pars(
{'iso_factor': {
'days': tti_day_august,
'vals': iso_vals1
}})
]
# 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
#s_prob=% of sympomatic that are tested only as part of TI strategies; we fit s_prob_may to data
s_prob_march = 0.007
s_prob_april = 0.01
s_prob_may = 0.0139
#s_prob_june =0.0139
a_prob = 0.0
#a_prob_june =0.0135
q_prob = 0.0
t_delay = 1.0
iso_vals = [{k: 0.1 for k in 'hswc'}]
march_tests = cv.test_prob(symp_prob=s_prob_march,
asymp_prob=a_prob,
symp_quar_prob=q_prob,
asymp_quar_prob=q_prob,
start_day=tc_day,
end_day=te_day,
test_delay=t_delay)
april_tests = cv.test_prob(symp_prob=s_prob_april,
asymp_prob=a_prob,
symp_quar_prob=q_prob,
asymp_quar_prob=q_prob,
start_day=te_day,
end_day=tt_day,
test_delay=t_delay)
may_tests = cv.test_prob(symp_prob=s_prob_may,
asymp_prob=0.00075,
symp_quar_prob=q_prob,
asymp_quar_prob=q_prob,
start_day=tt_day,
n_popfiles = 5
popfile = popfile_stem_change + str(
pars['rand_seed'] % n_popfiles) + '.ppl'
popfile_new = popfile_stem_change + str(
pars['rand_seed'] % n_popfiles) + '.ppl'
if scen == 'as_normal':
popfile = popfile
else:
popfile = popfile_new
sim = cv.Sim(pars,
popfile=popfile,
load_pop=True,
label=scen)
day_schools_reopen = sim.day('2020-09-01')
interventions = [
cv.test_prob(start_day='2020-07-01', **tp),
cv.contact_tracing(start_day='2020-07-01', **ct),
cv.clip_edges(days='2020-08-01',
changes=p['clip_edges'],
layers='w',
label='close_work'),
cv.clip_edges(days='2020-08-01',
changes=p['clip_edges'],
layers='c',
label='close_community'),
cv.change_beta(days='2020-08-01',
changes=0.75,
layers='c',
label='NPI_community'),
cv.change_beta(days='2020-08-01',
changes=0.75,
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
#tracing level at 42.35% in June; 47.22% in July, 44.4% in August and 49.6% in Septembre (until 16th Sep)
t_eff_june = 0.42
t_eff_july = 0.47
t_eff_august = 0.44
t_eff_sep = 0.50
t_eff_oct = 0.50
t_probs_june = {k:t_eff_june for k in 'hwsc'}
t_probs_july = {k:t_eff_july for k in 'hwsc'}
t_probs_august = {k:t_eff_august for k in 'hwsc'}
t_probs_sep = {k:t_eff_sep for k in 'hwsc'}
t_probs_oct = {k:t_eff_oct for k in 'hwsc'}
trace_d_1 = {'h':0, 's':1, 'w':1, 'c':2}
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.009, asymp_prob=0.0, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tc_day, end_day=te_day-1, test_delay=t_delay, test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_april, asymp_prob=0.0, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=te_day, end_day=tt_day-1, test_delay=t_delay, test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_may, asymp_prob=0.0075, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tt_day, end_day=tti_day-1, test_delay=t_delay, test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_june, asymp_prob=0.0175, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tti_day, end_day=tti_day_july-1, test_delay=t_delay, test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_july, asymp_prob=0.0175, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tti_day_july, end_day=tti_day_august-1, test_delay=t_delay, test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_august, asymp_prob=0.0175, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tti_day_august, end_day=tti_day_sep-1, test_delay=t_delay,test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_sept, asymp_prob=0.0175, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tti_day_sep, end_day=tti_day_oct-1, test_delay=t_delay, test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_oct, asymp_prob=0.0175, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tti_day_oct, test_delay=t_delay, test_sensitivity=0.97),
cv.dynamic_pars({'iso_factor': {'days': te_day, 'vals': iso_vals}}),
cv.contact_tracing(trace_probs=t_probs_june, trace_time=trace_d_1, start_day=tti_day, end_day=tti_day_july-1),
cv.contact_tracing(trace_probs=t_probs_july, trace_time=trace_d_1, start_day=tti_day_july, end_day=tti_day_august-1),
cv.contact_tracing(trace_probs=t_probs_august, trace_time=trace_d_1, start_day=tti_day_august, end_day=tti_day_sep-1),
cv.contact_tracing(trace_probs=t_probs_sep, trace_time=trace_d_1, start_day=tti_day_sep, end_day=tti_day_oct-1),
cv.contact_tracing(trace_probs=t_probs_sep, trace_time=trace_d_1, start_day=tti_day_oct),
cv.dynamic_pars({'iso_factor': {'days': tti_day, 'vals': iso_vals}}),
cv.dynamic_pars({'iso_factor': {'days': tti_day_august, 'vals': iso_vals1}}),
Compare simulating the entire population vs. dynamic rescaling vs. static rescaling.
'''
import sciris as sc
import covasim as cv
T = sc.tic()
p = sc.objdict() # Parameters
s = sc.objdict() # Sims
m = sc.objdict() # Multisims
# Interventions
tn = cv.test_num(start_day=40, daily_tests=1000,
symp_test=10) # Test a number of people
tp = cv.test_prob(start_day=30, symp_prob=0.1,
asymp_prob=0.01) # Test a number of people
cb = cv.change_beta(days=50, changes=0.5) # Change beta
# Properties that are shared across sims
basepop = 10e3
popinfected = 100
popscale1 = 10
popscale2 = 20 # Try a different population scale
which_interv = 2 # Which intervention to test
shared = sc.objdict(
n_days=120,
beta=0.015,
rand_seed=239487,
interventions=[cb, tn, tp],
)
def create_sim(test=0.0171, trace=0.47, seed=1):
start_day = '2020-01-21'
end_day = '2021-12-31'
data_path = 'UK_Covid_cases_august02.xlsx'
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop/pop_size)
pop_type = 'hybrid'
pop_infected = 1500
beta = 0.00593*1.2
asymp_factor = 2
contacts = {'h':3.0, 's':20, 'w':20, 'c':20}
pars = sc.objdict(
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,
)
# Create the baseline simulation
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1.0 # ages 0-10
sim['prognoses']['sus_ORs'][1] = 1.0 # ages 10-20
#%% Interventions
# Create the baseline simulation
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
ti_day = sim.day('2021-12-20') #schools interventions end date in December 2021
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
#change parameters here for different schools opening strategies with society opening
beta_days = ['2020-02-14', '2020-03-16', '2020-03-23', '2020-04-30', '2020-05-15', '2020-06-01', '2020-06-15', '2020-07-22', '2020-09-02', '2020-10-28', '2020-11-01', '2020-12-23', '2021-01-03', '2021-02-17', '2021-02-21', '2021-04-01', '2021-04-17', '2021-05-31', '2021-06-04', '2021-07-20', '2021-09-01', '2021-10-30', '2021-11-10', ti_day]
h_beta_changes = [1.00, 1.00, 1.29, 1.29, 1.29, 1.00, 1.00, 1.29, 1.00, 1.29, 1.00, 1.29, 1.00, 1.29, 1.00, 1.29, 1.00, 1.29, 1.00, 1.29, 1.00, 1.29, 1.00, 1.29]
s_beta_changes = [1.00, 0.90, 0.02, 0.02, 0.02, 0.23, 0.38, 0.00, 0.77, 0.00, 0.77, 0.00, 0.77, 0.00, 0.77, 0.00, 0.77, 0.00, 0.77, 0.00, 0.77, 0.00, 0.77, 0.00]
w_beta_changes = [0.90, 0.80, 0.20, 0.20, 0.20, 0.40, 0.50, 0.50, 0.60, 0.50, 0.60, 0.50, 0.60, 0.50, 0.60, 0.50, 0.60, 0.50, 0.60, 0.50, 0.60, 0.50, 0.60, 0.50]
c_beta_changes = [0.90, 0.80, 0.20, 0.20, 0.20, 0.40, 0.50, 0.50, 0.80, 0.62, 0.80, 0.62, 0.80, 0.62, 0.80, 0.62, 0.80, 0.62, 0.80, 0.62, 0.80, 0.62, 0.80, 0.62]
# Define the beta changes
h_beta = cv.change_beta(days=beta_days, changes=h_beta_changes, layers='h')
s_beta = cv.change_beta(days=beta_days, changes=s_beta_changes, layers='s')
w_beta = cv.change_beta(days=beta_days, changes=w_beta_changes, layers='w')
c_beta = cv.change_beta(days=beta_days, changes=c_beta_changes, layers='c')
#next line to save the intervention
interventions = [h_beta, w_beta, s_beta, c_beta]
# Tracing and enhanced testing strategy of symptimatics to change for phase plots
#testing
s_prob_march = 0.012
s_prob_april = 0.012
s_prob_may = 0.0165
s_prob_june = 0.0171
s_prob_july = 0.0171
#we want to vary s_prob_august to vary testing level
s_prob_august = test
t_delay = 1.0
iso_vals = [{k:0.1 for k in 'hswc'}]
#tracing level at 68% from June; 50% in July
#we want to vary t_eff_august to vary tracing level
t_eff_june = 0.42
t_eff_july = 0.47
t_eff_august = trace
t_probs_june = {k:t_eff_june for k in 'hwsc'}
t_probs_july = {k:t_eff_july for k in 'hwsc'}
t_probs_august = {k:t_eff_august for k in 'hwsc'}
trace_d_1 = {'h':0, 's':1, 'w':1, 'c':2}
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=s_prob_march, asymp_prob=0.0, symp_quar_prob=0.0, asymp_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, asymp_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=0.00075, symp_quar_prob=0.0, asymp_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=0.00075, symp_quar_prob=0.0, asymp_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=0.00075, symp_quar_prob=0.0, asymp_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=0.00075, symp_quar_prob=0.0, asymp_quar_prob=0.0, start_day=tti_day_august, test_delay=t_delay),
cv.dynamic_pars({'iso_factor': {'days': te_day, 'vals': iso_vals}}),
cv.contact_tracing(trace_probs=t_probs_june, trace_time=trace_d_1, start_day=tti_day, end_day=tti_day_july-1),
cv.contact_tracing(trace_probs=t_probs_july, trace_time=trace_d_1, start_day=tti_day_july, end_day=tti_day_august-1),
cv.contact_tracing(trace_probs=t_probs_august, trace_time=trace_d_1, start_day=tti_day_august),
]
# Finally, update the parameters
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
return sim
pars['n_days'] = 60
pars['quar_period'] = 140
sim = cv.Sim(pars=pars) # Setup sim
sim.initialize()
people = cv.make_people(sim) # Make the actual people for the simulation
inds_o5 = sc.findinds(people.age >= 10)
people.rel_sus[inds_o5] = 0
inds_u5 = sc.findinds(people.age < 10)
imm_frac = 0.8
inds_u5imm = inds_u5[sc.findinds(
np.random.uniform(low=0.0, high=1.0, size=len(inds_u5)) < imm_frac)]
people.rel_sus[inds_u5imm] = 0
sim.people = people
# sim['interventions'] = [cv.test_prob(symp_prob=1.00, asymp_prob=0.00)] # Test 100% of symptomatics and 0% of asymptomatics
sim['interventions'] = [
cv.test_prob(symp_prob=1.00, asymp_prob=0.00),
cv.contact_tracing()
]
sim.run()
sim.plot()
###########################################
#
#
# WARNING - What follows below is scary, poorly written scratch code. Don't judge me.
#
#
###########################################
list1 = ['a']
list2 = list1
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 create_sim(pars=None,
label=None,
use_safegraph=True,
show_intervs=False,
people=None,
school_reopening_pars=None,
teacher_test_scen=None):
''' Create a single simulation for further use '''
p = sc.objdict(
sc.mergedicts(
define_pars(which='best', kind='both',
use_safegraph=use_safegraph), pars))
if 'rand_seed' not in p:
seed = 1
print(f'Note, could not find random seed in {pars}! Setting to {seed}')
p['rand_seed'] = seed # Ensure this exists
if 'end_day' not in p:
end_day = '2020-05-30'
p['end_day'] = end_day
# Basic parameters and sim creation
pars = {
'pop_size': 225e3,
'pop_scale': 10,
'pop_type': 'synthpops',
'pop_infected': 400,
'beta': p.beta,
'start_day': '2020-01-27',
'end_day': p['end_day'],
'rescale': True,
'rescale_factor': 1.1,
'verbose': 0.1,
'rand_seed': p.rand_seed,
# 'analyzers' : cv.age_histogram(datafile=age_data_file),
'beta_layer': dict(h=p.bl_h, s=p.bl_s, w=p.bl_w, c=p.bl_c, l=p.bl_l),
}
pars.update({'n_days': cv.daydiff(pars['start_day'], pars['end_day'])})
# If supplied, use an existing people object
if people:
popfile = people
else:
# Generate the population filename
n_popfiles = 5
popfile = popfile_stem + str(pars['rand_seed'] % n_popfiles) + '.ppl'
popfile_change = popfile_stem_change + str(
pars['rand_seed'] % n_popfiles) + '.ppl'
# Check that the population file exists
if not os.path.exists(popfile):
errormsg = f'WARNING: could not find population file {popfile}! Please regenerate first'
raise FileNotFoundError(errormsg)
# Create and initialize the sim
sim = cv.Sim(pars,
label=label,
popfile=popfile,
load_pop=True,
datafile=epi_data_file
) # Create this here so can be used for test numbers etc.
# Define testing interventions
test_kwargs = dict(daily_tests=sim.data['new_tests'],
quar_test=1.0,
test_delay=2)
tn1 = cv.test_num(symp_test=p.tn1,
start_day='2020-01-27',
end_day='2020-03-23',
**test_kwargs,
label='tn1')
tn2 = cv.test_num(symp_test=p.tn2,
start_day='2020-03-24',
end_day='2020-04-14',
**test_kwargs,
label='tn2')
tn3 = cv.test_num(symp_test=p.tn3,
start_day='2020-04-15',
end_day=None,
**test_kwargs,
label='tn3')
interventions = [tn1, tn2, tn3]
ttq_scen = school_reopening_pars['ttq_scen']
if ttq_scen == 'lower':
tp = sc.objdict(
symp_prob=0.08,
asymp_prob=0.001,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.01,
trace_time=3.0,
)
elif ttq_scen == 'medium':
tp = sc.objdict(
symp_prob=0.12,
asymp_prob=0.0015,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.25,
trace_time=3.0,
)
elif ttq_scen == 'upper':
tp = sc.objdict(
symp_prob=0.24,
asymp_prob=0.003,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.5,
trace_time=3.0,
)
if ttq_scen is not None:
interventions += [
cv.test_prob(start_day='2020-06-10', **tp),
cv.contact_tracing(start_day='2020-06-10', **ct)
]
# Define beta interventions (for school reopening)
b_ch = sc.objdict()
b_days = [
'2020-03-04', '2020-03-12', '2020-03-23', '2020-04-25', '2020-08-30'
]
b_ch.w = [1.00, p.bc_wc1, p.bc_wc2, p.bc_wc3, p.bc_wc3]
b_ch.c = [1.00, p.bc_wc1, p.bc_wc2, p.bc_wc3, p.bc_wc3]
NPI_schools = school_reopening_pars['NPI_schools']
if NPI_schools is None:
b_ch.s = [1.00, 1.00, 1.00, 1.00, 1.00]
else:
b_ch.s = [1.00, 1.00, 1.00, 1.00, NPI_schools]
# LTCF intervention
b_days_l = np.arange(sim.day(b_days[0]), sim.day(b_days[2]) + 1)
b_ch_l = np.linspace(1.0, p.bc_lf, len(b_days_l))
interventions += [
cv.change_beta(days=b_days_l,
changes=b_ch_l,
layers='l',
label=f'beta_l')
]
for lkey, ch in b_ch.items():
interventions += [
cv.change_beta(days=b_days,
changes=b_ch[lkey],
layers=lkey,
label=f'beta_{lkey}')
]
# Define school closure interventions
network_change = school_reopening_pars['network_change']
if network_change:
popfile_new = popfile_change
else:
popfile_new = None
school_start_day = school_reopening_pars['school_start_day']
intervention_start_day = school_reopening_pars['intervention_start_day']
num_pos = None
test_prob = teacher_test_scen['test_prob']
trace_prob = teacher_test_scen['trace_prob']
mobility_file = school_reopening_pars['mobility_file']
interventions += [
cv.close_schools(day_schools_closed='2020-03-12',
start_day=school_start_day,
pop_file=popfile_new,
label='close_schools')
]
test_freq = teacher_test_scen['test_freq']
interventions += [
cv.reopen_schools(start_day=intervention_start_day,
num_pos=num_pos,
test=test_prob,
trace=trace_prob,
ili_prev=0.002,
test_freq=test_freq)
]
# SafeGraph intervention
interventions += make_safegraph(sim, mobility_file)
sim['interventions'] = interventions
analyzers = [cv.age_histogram(datafile=age_data_file)]
sim['analyzers'] += analyzers
# Don't show interventions in plots, there are too many
if show_intervs == False:
for interv in sim['interventions']:
interv.do_plot = False
# These are copied from parameters.py -- needed to get younger and 60-65 groups right
sim['prognoses']['age_cutoffs'] = np.array(
[0, 15, 20, 30, 40, 50, 65, 70, 80, 90]) # Age cutoffs (upper limits)
return sim
cv.check_version('0.27.9')
do_plot = 1
datafile = sc.thisdir(__file__, '../../example_data.csv')
pars = sc.objdict(diag_factor=1.0, n_days=30, pop_infected=100)
sim = cv.Sim(pars, datafile=datafile)
case = 2
testprobdict = {
0:
cv.test_prob(symp_prob=0,
asymp_prob=0,
test_sensitivity=1.0,
loss_prob=0.0,
test_delay=0,
start_day=0), # works, no diagnoses
1:
cv.test_prob(symp_prob=1,
asymp_prob=0,
test_sensitivity=1.0,
loss_prob=0.0,
test_delay=0,
start_day=0), # works, ~half diagnosed
2:
cv.test_prob(
symp_prob=0,
asymp_prob=1,
test_sensitivity=1.0,
loss_prob=0.0,
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
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 = 1.1,
#rel_severe_prob = 0.45,
#rel_crit_prob = 1.0,
#rel_death_prob=1.15,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1.0 # 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, 0.50, 0.70],
'2020-12-25': [1.50, 0.00, 0.20, 0.70],
'2020-12-26': [1.50, 0.00, 0.20, 0.70],
'2020-12-31': [1.50, 0.00, 0.20, 0.70],
'2021-01-01': [1.50, 0.00, 0.20, 0.70],
'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],
'2021-01-18': [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, 0.02, 0.30, 0.50],
'2021-04-05': [1.25, 0.02, 0.30, 0.50],
'2021-04-12': [1.25, 0.02, 0.40, 0.60],
'2021-04-19': [1.25, sbv, 0.40, 0.60],
'2021-04-26': [1.25, sbv, 0.40, 0.60],
'2021-05-03': [1.25, sbv, 0.40, 0.60],
'2021-05-10': [1.25, sbv, 0.40, 0.60],
'2021-05-17': [1.25, sbv, 0.50, 0.70],
'2021-05-21': [1.25, sbv, 0.50, 0.70],
'2021-05-28': [1.25, 0.02, 0.50, 0.70],
})
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({
'2021-05-03': [1.25, sbv, 0.40, 0.60],
'2021-05-10': [1.25, sbv, 0.40, 0.60],
'2021-05-17': [1.25, sbv, 0.50, 0.70],
'2021-05-21': [1.25, sbv, 0.50, 0.70],
'2021-05-28': [1.25, 0.02, 0.50, 0.70],
'2021-06-07': [1.25, sbv, 0.50, 0.70],
'2021-06-14': [1.25, sbv, 0.50, 0.70],
'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],
'2021-10-27': [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 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({
'2021-04-12': [1.25, 0.02, 0.40, 0.60],
'2021-04-19': [1.25, sbv, 0.40, 0.60],
'2021-04-26': [1.25, sbv, 0.40, 0.60],
'2021-05-03': [1.25, sbv, 0.40, 0.60],
'2021-05-10': [1.25, sbv, 0.40, 0.60],
'2021-05-17': [1.25, sbv, 0.50, 0.70],
'2021-05-21': [1.25, sbv, 0.50, 0.70],
'2021-05-28': [1.25, 0.02, 0.50, 0.70],
'2021-06-07': [1.25, sbv, 0.50, 0.70],
'2021-06-14': [1.25, sbv, 0.50, 0.70],
'2021-06-21': [1.25, sbv, 0.50, 0.70],
'2021-06-28': [1.25, sbv, 0.50, 0.70],
'2021-07-05': [1.25, sbv, 0.50, 0.70],
'2021-07-12': [1.25, sbv, 0.50, 0.70],
'2021-07-19': [1.25, 0.00, 0.50, 0.70],
'2021-07-26': [1.25, 0.00, 0.50, 0.70],
'2021-08-02': [1.25, 0.00, 0.50, 0.70],
'2021-08-16': [1.25, 0.00, 0.50, 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],
})
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.strain('b117',
days=np.arange(sim.day('2020-09-01'),
sim.day('2020-09-10')),
n_imports=100)
sim['strains'] += [b117]
# Add B.1.1351 strain
b1351 = cv.strain('b1351',
days=np.arange(sim.day('2020-11-20'),
sim.day('2020-11-30')),
n_imports=600)
sim['strains'] += [b1351]
# Add B.X.XXX strain starting middle of April
custom_strain = cv.strain(label='custom',
strain=cvp.get_strain_pars()['p1'],
days=np.arange(sim.day('2021-03-10'),
sim.day('2021-03-10')),
n_imports=600)
sim['strains'] += [custom_strain]
# seems like we need to do this to deal with cross immunity?!
sim.init_strains()
sim.init_immunity()
sim['immunity']
prior = {'wild': 0.5, 'b117': 0.8, 'b1351': 0.8, 'custom': 0.8}
pre = {'wild': 0.5, 'b117': 0.8, 'b1351': 0.8, 'custom': 0.8}
cross_immunities = cvp.get_cross_immunity()
for k, v in sim['strain_map'].items():
if v == 'custom':
for j, j_lab in sim['strain_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: 0.7 for k in 'hswc'}]
#isolation october
iso_vals3 = [{k: 0.7 for k in 'hswc'}]
#isolation november
iso_vals4 = [{k: 0.7 for k in 'hswc'}]
#isolation december
iso_vals5 = [{k: 0.7 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=0.012,
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=0.012,
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=0.012,
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 in
dose_pars = {
'nab_eff': {
'sus': {
'slope': 1.6,
'n_50': 0.05
}
},
'nab_init': {
'dist': 'normal',
'par1': -0.85,
'par2': 2
},
'nab_boost': 3,
'doses': 2,
'interval': 7 * 12,
'nab_interval': 14
}
strain_pars = {
'wild': 1.0,
'b117': 1 / 2.3,
'b1351': 1 / 9,
'p1': 1 / 2.9,
'custom': 1 / 2.3
}
vaccine = sc.mergedicts({'label': 'az_uk'},
sc.mergedicts(dose_pars, strain_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')))
]
def subtarget_60_75(sim):
inds = cv.true((sim.people.age >= 60) & (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_60_75,
days=np.arange(sim.day('2021-01-28'),
sim.day('2023-01-01')))
]
def subtarget_50_60(sim):
inds = cv.true((sim.people.age >= 50) & (sim.people.age < 60))
return {'inds': inds, 'vals': 0.010 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_50_60,
days=np.arange(sim.day('2021-02-10'),
sim.day('2023-01-01')))
]
def subtarget_40_50(sim):
inds = cv.true((sim.people.age >= 40) & (sim.people.age < 50))
return {'inds': inds, 'vals': 0.005 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_40_50,
days=np.arange(sim.day('2021-04-10'),
sim.day('2023-01-01')))
]
def subtarget_30_40(sim):
inds = cv.true((sim.people.age >= 30) & (sim.people.age < 40))
return {'inds': inds, 'vals': 0.003 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_30_40,
days=np.arange(sim.day('2021-05-10'),
sim.day('2023-01-01')))
]
def subtarget_18_30(sim):
inds = cv.true((sim.people.age >= 18) & (sim.people.age < 30))
return {'inds': inds, 'vals': 0.003 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.01,
subtarget=subtarget_18_30,
days=np.arange(sim.day('2021-06-10'),
sim.day('2023-01-01')))
]
analyzers = []
#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
'pars': {
'interventions': None,
}
},
'distance': {
'name': 'Social distancing',
'pars': {
'interventions': cv.change_beta(days=start_day, changes=0.7)
}
},
'ttq': {
'name': 'Test-trace-quarantine',
'pars': {
'interventions': [
cv.test_prob(start_day=start_day,
symp_prob=0.2,
asymp_prob=0.05,
test_delay=1.0),
cv.contact_tracing(start_day=start_day,
trace_probs=0.8,
trace_time=1.0),
]
}
},
}
# Run the scenarios -- this block is required for parallel processing on Windows
if __name__ == "__main__":
scens = cv.Scenarios(basepars=basepars,
metapars=metapars,
scenarios=scenarios)
