def test_age_hist():
sc.heading('Testing age histogram')
day_list = ["2020-03-20", "2020-04-20"]
age_analyzer = cv.age_histogram(days=day_list)
sim = cv.Sim(pars, analyzers=age_analyzer)
sim.run()
# Checks to see that compute windows returns correct number of results
sim.make_age_histogram() # Show post-hoc example
agehist = sim.get_analyzer()
agehist.compute_windows()
agehist.get() # Not used, but check get
agehist.get(day_list[1])
assert len(age_analyzer.window_hists) == len(
day_list), "Number of histograms should equal number of days"
# Check plot()
if do_plot:
plots = agehist.plot(windows=True)
assert len(plots) == len(
day_list), "Number of plots generated should equal number of days"
# Check daily age histogram
daily_age = cv.daily_age_stats()
sim = cv.Sim(pars, analyzers=daily_age)
sim.run()
return agehist
def test_age_hist():
sc.heading('Testing age histogram')
day_list = ["2020-03-20", "2020-04-20"]
age_analyzer = cv.age_histogram(days=day_list)
sim = cv.Sim(pars, analyzers=age_analyzer)
sim.run()
# Checks to see that compute windows returns correct number of results
agehist = sim['analyzers'][0]
agehist.compute_windows()
assert len(age_analyzer.window_hists) == len(day_list), "Number of histograms should equal number of days"
# checks compute_windows and plot()
plots = agehist.plot(windows=True)
assert len(plots) == len(day_list), "Number of plots generated should equal number of days"
return agehist
def create_sim(pars=None, use_safegraph=True, label=None, show_intervs=False):
''' Create a single simulation for further use '''
p = sc.objdict(
sc.mergedicts(define_pars(which='best', 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': 300,
'beta': p.beta,
'start_day': '2020-01-27',
'end_day': '2020-06-08',
'rescale': True,
'rescale_factor': 1.1,
'verbose': p.get('verbose', 0.01),
'rand_seed': int(p.rand_seed),
'analyzers': cv.age_histogram(datafile=age_data_file),
'beta_layer': dict(h=3.0, s=0.6, w=0.6, c=0.3, l=1.5),
}
# Create and initialize the sim
if pars['verbose']:
print(f'Creating sim! safegraph={use_safegraph}, seed={p.rand_seed}')
sim = cv.Sim(pars,
label=label,
popfile=get_popfile(pars),
load_pop=True,
datafile=epi_data_file
) # Create this here so can be used for test numbers etc.
# Define testing interventions -- 97% sensitivity from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177629/
test_kwargs = dict(daily_tests=sim.data['new_tests'],
test_delay=2,
sensitivity=0.97,
subtarget=test_num_subtarg)
tn = cv.test_num(symp_test=p.tn,
start_day='2020-01-27',
end_day=None,
**test_kwargs,
label='tn')
interventions = [tn]
# Define beta interventions
sim.intervention_info = sc.objdict()
hwc_days = ['2020-02-24', '2020-03-23',
'2020-05-31'] # Change date here, 04-27 or 05-04
hwc_days = sim.day(hwc_days)
b_wc_ch = [1.0, p.bc_wc1, p.get('bc_wc2', p.bc_wc1)
] # To allow either one or two beta change parameters
b_h_ch = [1.0, 1.1, 1.1] # Optional household
all_b_days = np.arange(hwc_days[0], hwc_days[-1] + 1) # Full time series
all_ch_wc = np.interp(all_b_days, hwc_days,
b_wc_ch) # Linearly interpolate
all_ch_h = np.interp(all_b_days, hwc_days, b_h_ch) # Linearly interpolate
interventions += [
cv.change_beta(days=all_b_days,
changes=all_ch_h,
layers='h',
label='beta_h')
]
lkeys = ['w', 'c', 's'] if use_safegraph else [
'w', 'c'
] # Skip schools if not using SafeGraph
for lkey in lkeys: # Assume schools also use masks, etc. so have same non-movement beta change
cb = cv.change_beta(days=all_b_days,
changes=all_ch_wc,
layers=lkey,
label=f'beta_{lkey}')
interventions += [cb]
sim.intervention_info.bc = sc.objdict({
'days': all_b_days,
'changes': all_ch_wc
}) # Store for plotting later
# LTCF beta change
b_l_days = ['2020-02-24', '2020-03-23']
b_l_days = np.arange(sim.day(b_l_days[0]), sim.day(b_l_days[1]))
b_l_ch = np.linspace(1.0, p.bc_lf, len(b_l_days))
interventions += [
cv.change_beta(days=b_l_days,
changes=b_l_ch,
layers='l',
label='beta_l')
]
sim.people.contacts['c'] = remove_ltcf_community(
sim) # Remove community contacts from LTCF
# SafeGraph intervention & tidy up
if use_safegraph:
interventions += make_safegraph(sim)
else:
interventions += [
cv.clip_edges(days='2020-03-12',
changes=0.1,
layers='s',
label='clip_s')
]
sim['interventions'] = interventions
# Don't show interventions in plots, there are too many
for interv in sim['interventions']:
interv.do_plot = False
# These are copied from parameters.py -- modified to capture change in work status at age 65
sim['prognoses']['age_cutoffs'] = np.array(
[0, 10, 20, 30, 40, 50, 65, 70, 80, 90]) # Age cutoffs (upper limits)
return sim
def create_sim(pars=None,
label=None,
use_safegraph=True,
show_intervs=False,
people=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 = '2020-07-19'
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': '2020-07-19',
'rescale': True,
'rescale_factor': 1.1,
'verbose': 0,
'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'
# 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]
# Define beta interventions (for calibration)
b_ch = sc.objdict()
b_days = ['2020-03-04', '2020-03-12', '2020-04-25']
b_ch.s = [1.00, 0.00, 0.00]
b_ch.w = [p.bc_wc1, p.bc_wc2, p.bc_wc3]
b_ch.c = [p.bc_wc1, p.bc_wc2, p.bc_wc3]
for lkey, ch in b_ch.items():
interventions += [
cv.change_beta(days=b_days,
changes=b_ch[lkey],
layers=lkey,
label=f'beta_{lkey}')
]
# 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')
]
# SafeGraph intervention & tidy up
interventions += make_safegraph(sim)
sim['interventions'] = interventions
# 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,
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
'''
Demonstrate simple analyzer usage
'''
import covasim as cv
# Age histograms
sim = cv.Sim(interventions=cv.test_prob(0.5), analyzers=cv.age_histogram())
sim.run()
agehist = sim.get_analyzer(
) # Only one analyzer so we can retrieve it like this
agehist.plot()
# Transmission trees
tt = sim.make_transtree()
fig1 = tt.plot()
fig2 = tt.plot_histograms()
# A custom analyzer
def check_88(sim):
people_who_are_88 = sim.people.age.round(
) == 88 # Find everyone who's aged 88 (to the nearest year)
people_exposed = sim.people.exposed # Find everyone who's infected with COVID
people_who_are_88_with_covid = cv.true(
people_who_are_88 *
people_exposed) # Multiplication is the same as logical "and"
n = len(people_who_are_88_with_covid) # Count how many people there are
if n:
print(
f'Oh no! {n} people aged 88 have covid on timestep {sim.t} {"🤯"*n}'
# # vaccinating 50-65 years old
# interventions += [utils.two_dose_daily_delayed(300e3, start_day=tti_day_vac2, dose_delay=21, delay=12*7,
# take_prob=1.0, rel_symp=0.05,
# rel_trans=0.5, cumulative=[0.7, 1.0], dose_priority=[1, 0.1],
# priority_days=[tti_day_vac2, tti_day_vac3], age_priority=[50,40])]
#vaccinating 18-50 years old
#interventions += [utils.two_dose_daily_delayed(300e3, start_day=tti_day_vac3, dose_delay=21, delay=7*7,
# take_prob=1.0, rel_symp=0.05,
# rel_trans=0.5, cumulative=[0.7, 1.0], dose_priority=[1, 0.1],
# priority_days=[tti_day_vac3, tti_day_vac4], age_priority=[50,18])]
analyzers = []
analyzers += [utils.record_dose_flows(vacc_class=utils.two_dose_daily_delayed)]
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
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}
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,
analyzers=[
cv.age_histogram(datafile='uk_deaths_by_age.xlsx',
edges=np.concatenate([
np.linspace(0, 90, 19),
np.array([100])
]))
]
#rel_death_prob=1.5,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 0.5 # 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 = 0.62 / (
1 + np.exp(-0.069 * (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 * 1.60 + (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=7 * 7,
take_prob=1.0,
rel_symp=0.05,
rel_trans=0.9,
cumulative=[0.7, 1.0],
dose_priority=[1, 0.1])
]
analyzers = sim['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
'''
Confirm that with default settings, all analyzers can be exported as JSONs.
'''
import sciris as sc
import covasim as cv
datafile = sc.thisdir(__file__, aspath=True).parent / 'example_data.csv'
# Create and runt he sim
sim = cv.Sim(analyzers=[
cv.snapshot(days='2020-04-04'),
cv.age_histogram(),
cv.daily_age_stats(),
cv.daily_stats()
],
datafile=datafile)
sim.run()
# Compute extra analyzers
tt = sim.make_transtree()
fit = sim.compute_fit()
# Construct list of all analyzers
analyzers = sim['analyzers'] + [tt, fit]
# Make jsons
jsons = {}
for an in analyzers:
print(f'Working on analyzer {an.label}...')
jsons[an.label] = an.to_json()
'''
Test the age-histogram analyzer.
'''
import covasim as cv
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
)
sim = cv.Sim(pars, analyzers=cv.age_histogram(datafile='example_age_data.csv'))
sim.run()
agehist = sim['analyzers'][0]
hists = agehist.get()
agehist.plot()
sim = cv.Sim(pars, analyzers=cv.age_histogram(days=['2020-04-01', 'end']))
sim.run()
agehist = sim['analyzers'][0]
agehist.plot(windows=True)
def create_sim(seed):
beta = 0.014 #0.011
pop_infected = 10 # initial cases of infection
start_day = '2020-02-01'
#end_day = '2020-11-30'
#end_day = '2021-03-31'
end_day = '2021-06-30'
data_file = df
# Set the parameters
total_pop = 5.8e6 # Denmark population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
asymp_factor = 2
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,
rescale=True,
verbose=0.1,
rand_seed=seed,
)
# Create the baseline simulation
sim = cv.Sim(pars=pars,
datafile=data_file,
location='denmark',
analyzers=cv.age_histogram(
edges=[0, 16, 65, 130],
states=['exposed', 'symptomatic',
'dead'])) #edges = [0,16,65,130],
relative_death = cv.dynamic_pars(rel_death_prob=dict(days=[
sim.day('2020-02-01'),
sim.day('2020-06-09'),
sim.day('2020-09-17')
],
vals=[1.3, 0.8, 0.4]))
interventions = [relative_death]
### Change beta ###
beta_days = [
'2020-03-15', '2020-04-15', '2020-05-10', '2020-06-22', '2020-07-20',
'2020-08-22', '2020-09-01', '2020-09-22', '2020-10-01', '2020-10-15',
'2020-11-01', '2020-11-20', '2020-11-30', '2020-12-14', '2021-01-01',
'2021-03-01', '2021-04-06'
]
h_beta_changes = [
1.10, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.10, 1.10, 1.10, 1.20,
1.30, 1.20, 1.10, 1.00, 1.00
]
s_beta_changes = [
0.80, 0.50, 0.50, 0.40, 0.60, 0.60, 1.00, 0.80, 0.80, 1.00, 0.80, 1.00,
1.20, 1.00, 0.80, 0.70, 0.60
]
w_beta_changes = [
0.80, 0.50, 0.50, 0.40, 0.60, 0.60, 1.00, 0.80, 0.80, 1.00, 0.80, 1.00,
1.20, 1.00, 0.80, 0.70, 0.50
]
c_beta_changes = [
0.90, 0.60, 0.50, 0.70, 0.90, 0.80, 1.00, 0.70, 0.70, 1.00, 0.80, 1.10,
1.30, 1.10, 1.00, 0.80, 0.70
]
# 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')
### edge clipping ###
clip_days = [
'2020-03-15', '2020-04-15', '2020-05-10', '2020-06-08', '2020-06-22',
'2020-08-17', '2020-09-01', '2020-09-15', '2020-10-05', '2020-11-05',
'2020-11-20', '2020-12-09', '2020-12-19', '2020-12-25', '2021-01-04',
'2021-02-01', '2021-03-01', '2021-04-06'
]
s_clip_changes = [
0.01, 0.20, 0.40, 0.70, 0.05, 0.10, 0.90, 0.80, 0.70, 0.70, 0.70, 0.40,
0.05, 0.05, 0.05, 0.30, 0.50, 0.80
]
w_clip_changes = [
0.10, 0.30, 0.50, 0.70, 0.60, 0.80, 1.00, 0.80, 0.70, 0.70, 0.70, 0.60,
0.40, 0.10, 0.50, 0.60, 0.60, 0.80
]
c_clip_changes = [
0.20, 0.40, 0.60, 0.85, 1.00, 1.00, 1.00, 0.80, 0.80, 0.90, 0.90, 0.70,
0.80, 0.50, 0.60, 0.70, 0.80, 0.90
]
# Define the edge clipping
s_clip = cv.clip_edges(days=clip_days, changes=s_clip_changes, layers='s')
w_clip = cv.clip_edges(days=clip_days, changes=w_clip_changes, layers='w')
c_clip = cv.clip_edges(days=clip_days, changes=c_clip_changes, layers='c')
interventions += [h_beta, w_beta, s_beta, c_beta, w_clip, s_clip, c_clip]
# Add a new change in beta to represent the takeover of the new variant B.1.1.7
nv_days = np.linspace(sim.day('2020-12-14'), sim.day('2021-03-28'), 15 * 7)
nv_prop = 0.952 / (1 + np.exp(-0.099 *
(nv_days - sim.day('2020-12-14') - 59)))
nv_change = nv_prop * 1.5 + (1 - nv_prop) * 1.0 #r = 1.5
nv_beta = cv.change_beta(days=nv_days, changes=nv_change)
c = np.r_[0.8 * np.ones(sim.day('2021-02-13') - sim.day('2020-12-14')),
0.4 * np.ones(sim.day('2021-03-29') - sim.day('2021-02-13'))]
relative_severe = cv.dynamic_pars(rel_severe_prob=dict(
days=nv_days, vals=nv_prop * 1.2 + (1 - nv_prop) * 1))
relative_critical = cv.dynamic_pars(rel_crit_prob=dict(
days=nv_days, vals=nv_prop * 1.2 + (1 - nv_prop) * 1))
relative_death_nv = cv.dynamic_pars(rel_death_prob=dict(
days=nv_days, vals=nv_prop * c * 1.2 + (1 - nv_prop) * c))
interventions += [
nv_beta, relative_severe, relative_critical, relative_death_nv
]
# import infections from 2020-02-20 to 2020-03-01
imports1 = cv.dynamic_pars(n_imports=dict(days=[25, 35], vals=[2, 0]))
imports2 = cv.dynamic_pars(n_imports=dict(days=[171, 190], vals=[2, 0]))
interventions += [imports1, imports2]
iso_vals = [{
'h': 0.5,
's': 0.05,
'w': 0.05,
'c': 0.1
}] #dict(h=0.5, s=0.05, w=0.05, c=0.1)
interventions += [
cv.dynamic_pars(
{'iso_factor': {
'days': sim.day('2020-03-15'),
'vals': iso_vals
}})
]
iso_vals2 = [{'h': 0.7, 's': 0.1, 'w': 0.1, 'c': 0.3}]
interventions += [
cv.dynamic_pars(
{'iso_factor': {
'days': sim.day('2021-05-01'),
'vals': iso_vals2
}})
]
# From May 12, starting tracing and isolation strategy
tracing_prob = dict(h=1.0, s=0.5, w=0.5, c=0.2)
trace_time = {'h': 0, 's': 1, 'w': 1, 'c': 2}
interventions += [
cv.contact_tracing(trace_probs=tracing_prob,
trace_time=trace_time,
start_day='2020-05-12')
]
interventions += [
cv.test_num(daily_tests=sim.data['new_tests'],
start_day=0,
end_day=sim.day('2021-03-31'),
test_delay=1,
symp_test=50,
sensitivity=0.97,
subtarget=prior_test)
]
vaccine1 = vaccine_plan(daily1,
start_day='2021-01-06',
end_day='2021-01-24',
delay=22,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine2 = vaccine_plan(daily2,
start_day='2021-01-25',
end_day='2021-02-15',
delay=27,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine3 = vaccine_plan(daily3,
start_day='2021-02-16',
end_day='2021-03-07',
delay=24,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine4 = vaccine_plan(daily4,
start_day='2021-03-08',
end_day='2021-04-10',
delay=37,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine5 = vaccine_plan(vn,
start_day='2021-04-11',
end_day='2021-06-30',
delay=37,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
interventions += [
cv.test_num(daily_tests=t,
start_day=sim.day('2021-04-01'),
end_day=sim.day('2021-06-30'),
test_delay=1,
symp_test=50,
sensitivity=0.97,
subtarget=prior_test)
]
interventions += [vaccine1, vaccine2, vaccine3, vaccine4, vaccine5]
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
sim.initialize()
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
