def test_tracing_dial(self):
sim = cv.Sim(SIM_PARAMS, popfile=popfile, load_pop=True)
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'),
cv.reopen_schools(start_day='2020-08-05',
ili_prev=0,
num_pos=None,
trace=0.1,
label='reopen_schools')
]
sim['interventions'] = interventions
sim.run()
cum_infected_a = sim.results['cum_infections']
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'),
cv.reopen_schools(start_day='2020-08-05',
ili_prev=0,
num_pos=None,
trace=0.4,
label='reopen_schools')
]
sim['interventions'] = interventions
sim.run()
cum_infected_b = sim.results['cum_infections']
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'),
cv.reopen_schools(start_day='2020-08-05',
ili_prev=0,
num_pos=None,
trace=0.7,
label='reopen_schools')
]
sim['interventions'] = interventions
sim.run()
cum_infected_c = sim.results['cum_infections']
self.assertGreater(cum_infected_c, cum_infected_b)
self.assertGreater(cum_infected_b, cum_infected_a)
def test_testing_dial(self):
sim = cv.Sim(SIM_PARAMS, popfile=popfile, load_pop=True)
# Setting test to 0.1, trace to 0 so that trace doesn't mess up validation
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
test=0.1,
trace=0,
ili_prev=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
diagnoses_a = reopen_schools.num_diagnosed
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
test=0.4,
ili_prev=0,
trace=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
diagnoses_b = reopen_schools.num_diagnosed
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
test=0.8,
ili_prev=0,
trace=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
diagnoses_c = reopen_schools.num_diagnosed
# This requires a bit of fenegling and testing to determine reasonable relative
# values
self.assertGreater(diagnoses_a, diagnoses_b)
self.assertGreater(diagnoses_b, diagnoses_c)
def test_ili_schooldays(self):
SIM_PARAMS = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 0,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-08-30',
'rand_seed': 0,
}
sim = cv.Sim(SIM_PARAMS, popfile=popfile, load_pop=True)
reopen_schools = cv.reopen_schools(start_day={
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
},
ili_prev=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
# No students should be left undiagnosed
num_school_days_lost_a = sim.school_info['school_days_lost']
reopen_schools = cv.reopen_schools(start_day={
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
},
ili_prev=0.8,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
# Can add more cases for greater granularity of testing
num_school_days_lost_b = sim.school_info['school_days_lost']
self.assertGreater(num_school_days_lost_b, num_school_days_lost_a)
def test_no_infected(self):
SIM_PARAMS2 = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 0,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-09-01',
'rand_seed': 0,
}
popfile = f'inputs/kc_synthpops_clustered_withstaff_10e3.ppl'
sim = cv.Sim(SIM_PARAMS2, popfile=popfile, load_pop=True)
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'),
cv.reopen_schools(start_day='2020-08-01',
test=0.99,
ili_prev=0,
num_pos=None,
label='reopen_schools'),
cv.change_beta(1, 0)
]
sim['interventions'] = interventions
sim.run()
num_school_days_lost = sim.school_info['school_days_lost']
num_student_school_days = sim.school_info['total_student_school_days']
#assertinhg that all school days are lost
self.assertEqual(num_school_days_lost, 0)
self.assertGreater(num_student_school_days, 0)
def test_numpos_limit(self):
SIM_PARAMS6 = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 100,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-08-10',
'rand_seed': 0,
}
sim = cv.Sim(SIM_PARAMS6, popfile=popfile, load_pop=True)
reopen_schools = cv.reopen_schools(start_day={
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
},
test=0.99,
ili_prev=0,
num_pos=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
self.assertEqual(reopen_schools.num_schools,
reopen_schools.closed.count(
True)) # Ensuring that all schools are closed
def test_testing_params(self):
SIM_PARAMS = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 100,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-09-01',
'rand_seed': 0,
}
sim = cv.Sim(SIM_PARAMS, popfile=popfile, load_pop=True)
reopen_schools = cv.reopen_schools(start_day={
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
},
test=1,
test_freq=1,
ili_prev=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
# No students should be left undiagnosed
self.assertEqual(reopen_schools.num_undiagnosed, 0)
def test_all_infected(self):
SIM_PARAMS3 = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 10e3,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-09-01',
'rand_seed': 0,
}
sim = cv.Sim(SIM_PARAMS3, popfile=popfile, load_pop=True)
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
test=0.99,
ili_prev=0,
num_pos=50,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
#asserting that all schools are closed
self.assertEqual(reopen_schools.num_schools,
reopen_schools.school_closures)
num_school_days_lost = sim.school_info['school_days_lost']
num_student_school_days = sim.school_info['total_student_school_days']
#assertinhg that all school days are lost
self.assertEqual(num_school_days_lost, num_student_school_days)
def test_iliprev_exceptions(self):
SIM_PARAMS6 = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 100,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-09-91',
'rand_seed': 0,
}
# testing higher value of ili_prev
sim = cv.Sim(SIM_PARAMS6, popfile=popfile, load_pop=True)
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
test=0.99,
ili_prev=2,
label='reopen_schools')
# testing negative value of ili_prev
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
self.assertRaises(
ValueError, sim.run
) # Need to either skip this test or fine tune error message
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
ili_prev=-1,
num_pos=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
self.assertRaises(ValueError, sim.run)
def test_freq_dial(self):
is_debugging = False
SIM_PARAMS8 = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 5e3,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-08-30',
'rand_seed': 0,
}
sim = cv.Sim(SIM_PARAMS8, popfile=popfile, load_pop=True)
# Testing daily, bidaily, and every third day testing, trace set to 0 so it doesn't mess with validation
# since if you have teachers tested and traced then infectious kiddos would be sent home and the infection rate would
# be substantially lower
results = []
for i in range(1, 3):
reopen_schools = cv.reopen_schools(start_day={
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
},
test_freq=i,
trace=0,
ili_prev=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
# May want to load school_info from differently saved json
#sim.to_json(output_filename=f"DEBUG_test_freq_{i}.json")
#with open(f"json_test_allinfected.json") as f:
# json_format = json.load(f)
results.append(sim.school_info['num_teachers_tested'])
# This requires a bit of fenegling and testing to determine reasonable relative
# values, but more frequent testing should lead to more diagnoses in absence of tracing
self.assertGreater(results[0], results[1])
self.assertGreater(results[1], results[2])
def test_numpos_days(self):
sim = cv.Sim(SIM_PARAMS, popfile=popfile, load_pop=True)
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
test=0.99,
num_pos=1,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
num_school_days_lost_a = sim.school_info['school_days_lost']
reopen_schools = cv.reopen_schools(start_day='2020-08-05',
test=0.99,
ili_prev=0,
num_pos=100,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
num_school_days_lost_b = sim.school_info['school_days_lost']
# This will require a bit of fenegling and testing to determine reasonable relative
# values
self.assertGreater(num_school_days_lost_a, num_school_days_lost_b)
'end_day': '2020-08-30',
'rand_seed': 0,
}
sim = cv.Sim(SIM_PARAMS, popfile=popfile, load_pop=True)
reopen_schools = cv.reopen_schools(start_day={
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
},
ili_prev=0,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01', label='close_schools'),
reopen_schools
]
sim['interventions'] = interventions
sim.run()
# No students should be left undiagnosed
num_school_days_lost_a = sim.school_info['school_days_lost']
reopen_schools = cv.reopen_schools(start_day={
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
def run_sim(params):
p = sc.objdict(
sc.mergedicts(define_pars(which='best', kind='both'), params))
if 'rand_seed' not in p:
seed = 1
print(
f'Note, could not find random seed in {params}! Setting to {seed}')
p['rand_seed'] = seed # Ensure this exists
popfile_stem = f'inputs/kc_synthpops_clustered_withstaff_seed'
tp = sc.objdict(
symp_prob=0.03,
asymp_prob=0.001,
symp_quar_prob=0.01,
asymp_quar_prob=0.001,
test_delay=5.0,
)
ct = sc.objdict(
trace_probs={
'w': 0.1,
'c': 0,
'h': 0.8,
's': 0.8
},
trace_time=5.0,
)
pars = {
'pop_size': 225e3,
'pop_scale': 10,
'pop_type': 'synthpops',
'pop_infected': p.pop_infected,
'rescale': True,
'rescale_factor': 1.1,
'verbose': 0,
'start_day': '2020-07-01',
'end_day': '2020-12-01',
'rand_seed': p.rand_seed,
}
n_popfiles = 5
popfile = popfile_stem + str(params['rand_seed'] % n_popfiles) + '.ppl'
sim = cv.Sim(pars, popfile=popfile, load_pop=True)
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,
layers='w',
label='NPI_work'),
cv.close_schools(day_schools_closed='2020-07-01',
start_day=None,
label='close_schools')
]
sim['interventions'] = interventions
for interv in sim['interventions']:
interv.do_plot = False
sim.run()
return (sim)
def test_lost_days_startday(self):
day_schools_open1 = {
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
}
day_schools_open2 = {
'pk': '2020-08-25',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
}
day_schools_open3 = {
'pk': '2020-08-25',
'es': '2020-08-25',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
}
day_schools_open4 = {
'pk': '2020-08-25',
'es': '2020-08-25',
'ms': '2020-08-25',
'hs': '2020-08-05',
'uv': '2020-08-05'
}
day_schools_open5 = {
'pk': '2020-08-25',
'es': '2020-08-25',
'ms': '2020-08-25',
'hs': '2020-08-25',
'uv': '2020-08-05'
}
day_schools_open6 = {
'pk': '2020-08-25',
'es': '2020-08-25',
'ms': '2020-08-25',
'hs': '2020-08-25',
'uv': '2020-08-25'
}
day_schools_open = [
day_schools_open1, day_schools_open2, day_schools_open3,
day_schools_open4, day_schools_open5, day_schools_open6
]
sim = cv.Sim(SIM_PARAMS, popfile=popfile, load_pop=True)
cum_infected_prev = -1
num_school_days_lost_prev = 10000000
for schedule in day_schools_open:
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
start_day='2020-08-05',
label='close_schools'),
cv.reopen_schools(start_day=schedule,
ili_prev=0,
num_pos=None,
trace=0.1,
label='reopen_schools')
]
sim['interventions'] = interventions
sim.run()
cum_infected_current = sim.results['cum_infections']
num_school_days_lost = sim.school_info['school_days_lost']
self.assertGreater(cum_infected_current, cum_infected_prev)
self.assertGreater(num_school_days_lost_prev, num_school_days_lost)
cum_infected_prev = cum_infected_current
num_school_days_lost_prev = num_school_days_lost
def test_different_schedules(self):
day_schools_open1 = {
'pk': '2020-08-05',
'es': '2020-09-05',
'ms': '2020-09-05',
'hs': '2020-09-05',
'uv': '2020-09-05'
}
day_schools_open2 = {
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-09-05',
'hs': '2020-09-05',
'uv': '2020-09-05'
}
day_schools_open3 = {
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-09-05',
'hs': '2020-09-05',
'uv': '2020-09-05'
}
day_schools_open4 = {
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-09-05'
}
day_schools_open5 = {
'pk': '2020-08-05',
'es': '2020-08-05',
'ms': '2020-08-05',
'hs': '2020-08-05',
'uv': '2020-08-05'
}
school_types = ['pk', 'es', 'ms', 'hs', 'uv']
schedules = [
day_schools_open1, day_schools_open2, day_schools_open3,
day_schools_open4, day_schools_open5
]
popfile = f'inputs/kc_synthpops_clustered_withstaff_10e3.ppl'
SIM_PARAMS5 = pars = {
'pop_size': 10e3,
'pop_type': 'synthpops',
'pop_infected': 100,
'verbose': 1,
'start_day': '2020-08-01',
'end_day': '2020-09-10',
'rand_seed': 0,
}
sim = cv.Sim(SIM_PARAMS5, popfile=popfile, load_pop=True)
opened_initial = 10000 #arbitrarily large number
for i, schedule in enumerate(schedules):
reopen_schools = cv.reopen_schools(
start_day=schedule,
test=
0.99, # high test in order to make the validation less prone to "stochastic effects"
ili_prev=0,
num_pos=None,
label='reopen_schools')
interventions = [
cv.close_schools(day_schools_closed='2020-08-01',
label='close_schools'), reopen_schools
]
sim['interventions'] = interventions
sim.run()
new_opened = reopen_schools.closed.count(
False) #ensuring that fewer and fewer schools closed
self.assertGreater(new_opened, initial_opened)
opened_initial = new_opened
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,
layers='w',
label='NPI_work'),
cv.close_schools(day_schools_closed=day_schools_close,
start_day=day_schools_open[i],
label='close_schools'),
cv.reopen_schools(start_day=intervention_start_day[i],
test=test_prob[i],
trace=trace_prob[i],
ili_prev=0.002,
schedule=schedule[i],
num_pos=None,
label='reopen_schools')
]
if rel_trans:
sim['prognoses']['trans_ORs'][0] = 0.5
if beta_layer:
sim['beta_layer']['s'] = 3
if NPI[i] is not None:
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
def create_sim(pars=None, use_safegraph=True, label=None, show_intervs=False, people=None, adjust_ORs=False,
num_pos=None, test_prob=None,
trace_prob=None, NPI_schools=None, test_freq=None, network_change=False, schedule=None,
school_start_day=None,
intervention_start_day=None, ttq_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
# Basic parameters and sim creation
pars = {'pop_size': 225e3,
'pop_scale': 10,
'pop_type': 'synthpops',
'pop_infected': p.pop_infected,
'beta': p.beta,
'start_day': '2020-02-01',
'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,
states=['exposed', 'dead', 'tested', 'diagnosed', 'severe'])],
'beta_layer': dict(h=p.bl_h, s=p.bl_s, w=p.bl_w, c=p.bl_c, l=p.bl_l),
}
# 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
print(f'Creating sim! safegraph={use_safegraph}, seed={p.rand_seed}')
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.
interventions = []
# Testing bins
df = pd.read_csv(age_series_file)
age_bins = [0, 20, 40, 60, 80, np.inf]
test_num_subtargs = [ftest_num_subtarg1, ftest_num_subtarg2, ftest_num_subtarg3, ftest_num_subtarg4,
ftest_num_subtarg5]
for ia in range(len(age_bins) - 1):
label = '{}-{}'.format(age_bins[ia], age_bins[ia + 1] - 1)
df_ = df[(df['age'] >= age_bins[ia]) & (df['age'] < age_bins[ia + 1])]
# sum for the dates
df_ = df_.groupby('date').sum()
df_['age'] = age_bins[ia]
df_['datetime'] = [sc.readdate(d) for d in df_.index.values]
df_ = df_.set_index('datetime')
# make sure we have all the days we care about
new_index = pd.date_range(df_.index[0], df_.index[-1], freq='1d')
df_ = df_.reindex(new_index, fill_value=0.0, method='nearest')
test_kwargs = dict(daily_tests=df_['new_tests'], quar_test=1.0, test_delay=2, subtarget=test_num_subtargs[ia])
interventions += [cv.test_num(symp_test=p.tn1, start_day='2020-01-27', end_day='2020-03-23', **test_kwargs,
label='tn1 ' + label)]
interventions += [cv.test_num(symp_test=p.tn2, start_day='2020-03-24', end_day='2020-04-14', **test_kwargs,
label='tn2 ' + label)]
interventions += [cv.test_num(symp_test=p.tn3, start_day='2020-04-15', end_day='2020-05-07', **test_kwargs,
label='tn3 ' + label)]
interventions += [cv.test_num(symp_test=p.tn4, start_day='2020-05-08', end_day='2020-06-04', **test_kwargs,
label='tn4 ' + label)]
interventions += [cv.test_num(symp_test=p.tn5, start_day='2020-06-17', end_day='2020-06-18', **test_kwargs,
label='tn5 ' + label)]
interventions += [cv.test_num(symp_test=p.tn6, start_day='2020-06-19', end_day=None, **test_kwargs,
label='tn6 ' + label)]
# Seed in LTCF
interventions += [seed_ltcf(days=[15], seeds=[p.lseeds])]
# Define beta interventions
b_days = ['2020-03-02',
'2020-03-12',
'2020-03-23',
'2020-04-15',
'2020-05-08',
'2020-06-05',
'2020-06-19'
]
# Home and school beta changes
interventions += [
cv.change_beta(days=b_days[1], changes=p.bc_s, layers='s', label="beta_s")
]
# Work and community beta changes
b_days_wc = np.arange(sim.day(b_days[0]), sim.day(b_days[1]) + 1).tolist() + b_days[2:]
b_ch_wc = np.linspace(1.0, p.bc_wc1, len(b_days_wc) - 5).tolist() + [p.bc_wc2, p.bc_wc3, p.bc_wc4, p.bc_wc5,
p.bc_wc6]
for lkey in ['w', 'c']:
interventions += [cv.change_beta(days=b_days_wc, changes=b_ch_wc, layers=lkey, label=f'beta_{lkey}')]
# LTCF beta change
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='beta_l')]
# sim.people.contacts['c'] = remove_ltcf_community(sim) # Remove community contacts from LTCF
# Age-based beta changes
b_age_days = ["2020-05-08", "2020-06-05", "2020-06-19"]
b_age_changes = [[p.bc_age_u10_a, # 0
p.bc_age_10_20_a, # 10
p.bc_age_20_30_a, # 20
p.bc_age_30_40_a, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_a, # 65
p.bc_age_65u_a, # 70
p.bc_age_65u_a, # 80
p.bc_age_65u_a, # 90
],
[p.bc_age_u10_b, # 0
p.bc_age_10_20_b, # 10
p.bc_age_20_30_b, # 20
p.bc_age_30_40_b, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_b, # 65
p.bc_age_65u_b, # 70
p.bc_age_65u_b, # 80
p.bc_age_65u_b, # 90
],
[p.bc_age_u10_c, # 0
p.bc_age_10_20_c, # 10
p.bc_age_20_30_c, # 20
p.bc_age_30_40_c, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_c, # 65
p.bc_age_65u_c, # 70
p.bc_age_65u_c, # 80
p.bc_age_65u_c, # 90
]
]
interventions += [beta_change_age.change_beta_age(b_age_days, b_age_changes)]
# SafeGraph intervention & tidy up
if use_safegraph:
interventions += make_safegraph(sim)
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='2020-07-10', **tp), cv.contact_tracing(start_day='2020-07-10', **ct)]
if network_change:
popfile_new = popfile_change
else:
popfile_new = None
if NPI_schools is not None:
interventions += [cv.change_beta(days=sim.day('2020-09-01'), changes=NPI_schools, layers='s')]
interventions += [cv.close_schools(day_schools_closed='2020-03-12', start_day=school_start_day,
pop_file=popfile_new)]
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, schedule=schedule)]
sim['interventions'] = interventions
# Don't show interventions in plots, there are too many
for interv in sim['interventions']:
interv.do_plot = False
# Prognoses (particularly to severe/hospitalizations) need attention
prognoses = sc.dcp(cv.get_prognoses())
prognoses['severe_probs'] *= prognoses[
'symp_probs'] # Conditional probability of symptoms becoming severe, given symptomatic
prognoses['crit_probs'] *= prognoses[
'severe_probs'] # Conditional probability of symptoms becoming critical, given severe
prognoses['death_probs'] *= prognoses['crit_probs'] # Conditional probability of dying, given critical symptoms
prognoses.update({'age_cutoff': np.array([0, 10, 20, 30, 40, 50, 65, 70, 80, 90])})
prognoses.update({'severe_probs': np.array([1, 1, 1, p.rsp1, p.rsp1, p.rsp1, p.rsp1, p.rsp1, p.rsp2, p.rsp2]) *
prognoses['severe_probs']})
prognoses['death_probs'] /= prognoses['crit_probs'] # Conditional probability of dying, given critical symptoms
prognoses['crit_probs'] /= prognoses[
'severe_probs'] # Conditional probability of symptoms becoming critical, given severe
prognoses['severe_probs'] /= prognoses[
'symp_probs'] # Conditional probability of symptoms becoming severe, given symptomatic
sim.pars.update({'prognoses': prognoses})
return sim