def sample_model_parameters(what, age, severity=None, variables=None):
age_to_group = make_age_groups()
max_age = variables['max_age']
age_structure = pd.Series([1] * (max_age + 1), index=range(0, max_age + 1))
age_groups = list(np.unique(age_to_group))
pop_params = dict(age_structure=age_structure,
contacts_per_day=get_contacts_per_day(),
age_groups=dict(labels=age_groups,
age_indices=[
age_groups.index(x)
for x in age_to_group
]))
hc_params = dict(hospital_beds=0, icu_units=0)
disease_params = create_disease_params(variables)
context = model.Context(
population_params=pop_params,
healthcare_params=hc_params,
disease_params=disease_params,
start_date='2020-01-01',
)
if variables['sample_limit_mobility'] != 0:
context.apply_intervention('limit-mobility',
variables['sample_limit_mobility'])
samples = context.sample(what, age, severity)
if what == 'infectiousness':
s = pd.Series(index=samples['day'], data=samples['val'])
s = s[s != 0].sort_index()
return s
s = pd.Series(samples)
c = s.value_counts().sort_index()
if what == 'symptom_severity':
c.index = c.index.map(model.SEVERITY_TO_STR)
if True:
c /= c.sum()
for a, b in c.iteritems():
# print(' (%d, %.2f),' % (a, b))
print(' (%s, %.2f),' % (a, b))
import matplotlib.pyplot as plt
fig = plt.figure()
print('Mean: %f, median: %f' % (s.mean(), s.median()))
plt.plot(c)
plt.show()
return c
def sample_model_parameters(what, age, severity=None, variables=None):
avg_contacts_per_day = get_contacts_for_country()
age_counts = [1]
pop = model.Population(age_counts, list(avg_contacts_per_day.items()))
hc = model.HealthcareSystem(
beds=0,
icu_units=0,
p_detected_anyway=variables['p_detected_anyway'] / 100)
disease = create_disease(variables)
context = model.Context(pop, hc, disease, start_date='2020-01-01')
if variables['sample_limit_mobility'] != 0:
context.apply_intervention('limit-mobility',
variables['sample_limit_mobility'])
samples = context.sample(what, age, severity)
if what == 'infectiousness':
s = pd.Series(index=samples['day'], data=samples['val'])
s = s[s != 0].sort_index()
return s
s = pd.Series(samples)
c = s.value_counts().sort_index()
if what == 'symptom_severity':
c.index = c.index.map(model.SEVERITY_TO_STR)
if False:
# c /= c.sum()
for a, b in c.iteritems():
print(' (%d, %.2f),' % (a, b))
import matplotlib.pyplot as plt
fig = plt.figure()
print('Mean: %f, median: %f' % (s.mean(), s.median()))
plt.plot(c)
plt.show()
return c
def simulate_individuals(variables,
step_callback=None,
callback_day_interval=1):
pc = PerfCounter()
age_structure = get_population_for_area().sum(axis=1)
ipc = get_initial_population_condition()
age_to_group = make_age_groups()
age_groups = list(np.unique(age_to_group))
pop_params = dict(
age_structure=age_structure,
contacts_per_day=get_contacts_per_day(),
initial_population_condition=ipc,
age_groups=dict(
labels=age_groups,
age_indices=[age_groups.index(x) for x in age_to_group]),
imported_infection_ages=variables['imported_infection_ages'],
)
df = get_contacts_per_day()
hc_params = dict(hospital_beds=variables['hospital_beds'],
icu_units=variables['icu_units'])
disease_params = create_disease_params(variables)
context = model.Context(population_params=pop_params,
healthcare_params=hc_params,
disease_params=disease_params,
start_date=variables['start_date'],
random_seed=variables['random_seed'])
start_date = date.fromisoformat(variables['start_date'])
ivs = get_active_interventions(variables)
for iv in ivs:
context.add_intervention(iv)
pc.measure()
days = variables['simulation_days']
date_index = pd.date_range(start_date, periods=days)
df = pd.DataFrame(
columns=POP_ATTRS + STATE_ATTRS + EXPOSURES_ATTRS +
['us_per_infected'],
index=date_index,
)
ag_array = np.empty((days, len(POP_ATTRS), len(age_groups)), dtype='i')
for day in range(days):
s = context.generate_state()
today_date = (start_date + timedelta(days=day)).isoformat()
for idx, attr in enumerate(POP_ATTRS):
ag_array[day, idx, :] = s[attr]
rec = {attr: s[attr].sum() for attr in POP_ATTRS}
for state_attr in STATE_ATTRS:
rec[state_attr] = s[state_attr]
for place, nr in s['daily_contacts'].items():
key = 'exposures_%s' % place
assert key in df.columns
rec[key] = nr
rec['us_per_infected'] = pc.measure(
) * 1000 / rec['infected'] if rec['infected'] else 0
if False:
st = '\n%-15s' % today_date
for ag in age_groups:
st += '%8s' % ag
print(st)
for attr in ('all_detected', 'in_ward', 'dead', 'cum_icu'):
st = '%-15s' % attr
t = s[attr].sum()
for val in s[attr]:
st += '%8.2f' % ((val / t) * 100)
print(st)
if False:
dead = context.get_population_stats('dead')
all_infected = context.get_population_stats('all_infected')
detected = context.get_population_stats('all_detected')
age_groups = pd.interval_range(0, 80, freq=10, closed='left')
age_groups = age_groups.append(
pd.Index([pd.Interval(80, 100, closed='left')]))
s = pd.Series(dead)
dead_by_age = s.groupby(pd.cut(s.index, age_groups)).sum()
dead_by_age.name = 'dead'
s = pd.Series(all_infected)
infected_by_age = s.groupby(pd.cut(s.index, age_groups)).sum()
infected_by_age.scenario_name = 'infected'
s = pd.Series(detected)
detected_by_age = s.groupby(pd.cut(s.index, age_groups)).sum()
detected_by_age.name = 'detected'
print(dead_by_age / sum(dead_by_age) * 100)
print(infected_by_age / sum(infected_by_age) * 100)
print(detected_by_age / sum(detected_by_age) * 100)
#zdf = pd.DataFrame(dead_by_age)
#zdf['infected'] = infected_by_age
#zdf['ifr'] = zdf.dead.divide(zdf.infected.replace(0, np.inf)) * 100
#print(zdf)
df.loc[today_date] = rec
by_age_group = POP_ATTRS
if step_callback is not None and (day % callback_day_interval == 0
or day == range(days) - 1):
ret = step_callback(df)
if not ret:
raise ExecutionInterrupted()
context.iterate()
if False:
import cProfile
import pstats
cProfile.runctx("context.iterate()", globals(), locals(),
"profile.prof")
s = pstats.Stats("profile.prof")
s.strip_dirs().sort_stats("cumtime").print_stats()
arr = ag_array.flatten()
adf = pd.DataFrame(arr,
index=pd.MultiIndex.from_product(
[date_index, POP_ATTRS, age_groups],
names=['date', 'attr', 'age_group']),
columns=['pop'])
adf = adf.unstack('attr').unstack('age_group')
adf.columns = adf.columns.droplevel()
return df, adf
def simulate_individuals(variables, step_callback=None):
pc = PerfCounter()
df = get_population_for_area().sum(axis=1)
ages = df.index.values
counts = df.values
avg_contacts_per_day = get_contacts_for_country()
hc_cap = (variables['hospital_beds'], variables['icu_units'])
max_age = max(ages)
age_counts = np.array(np.zeros(max_age + 1, dtype=np.int32))
for age, count in zip(ages, counts):
age_counts[age] = count
pop = model.Population(age_counts, list(avg_contacts_per_day.items()))
hc = model.HealthcareSystem(
beds=hc_cap[0],
icu_units=hc_cap[1],
p_detected_anyway=variables['p_detected_anyway'] / 100)
disease = create_disease(variables)
context = model.Context(pop,
hc,
disease,
start_date=variables['start_date'],
random_seed=variables['random_seed'])
start_date = date.fromisoformat(variables['start_date'])
for iv in variables['interventions']:
d = (date.fromisoformat(iv[1]) - start_date).days
if len(iv) > 2:
val = iv[2]
else:
val = 0
context.add_intervention(d, iv[0], val)
pc.measure()
days = variables['simulation_days']
df = pd.DataFrame(columns=POP_ATTRS + STATE_ATTRS + ['us_per_infected'],
index=pd.date_range(start_date, periods=days))
for day in range(days):
s = context.generate_state()
rec = {attr: sum(s[attr]) for attr in POP_ATTRS}
for state_attr in STATE_ATTRS:
rec[state_attr] = s[state_attr]
rec['us_per_infected'] = pc.measure(
) * 1000 / rec['infected'] if rec['infected'] else 0
"""
dead = context.get_population_stats('dead')
all_infected = context.get_population_stats('all_infected')
age_groups = pd.interval_range(0, 100, freq=10, closed='left')
s = pd.Series(dead)
dead_by_age = s.groupby(pd.cut(s.index, age_groups)).sum()
dead_by_age.name = 'dead'
s = pd.Series(all_infected)
infected_by_age = s.groupby(pd.cut(s.index, age_groups)).sum()
zdf = pd.DataFrame(dead_by_age)
zdf['infected'] = infected_by_age
zdf['ifr'] = zdf.dead.divide(zdf.infected.replace(0, np.inf)) * 100
print(zdf)
"""
d = start_date + timedelta(days=day)
df.loc[d] = rec
if step_callback is not None:
ret = step_callback(df)
if not ret:
raise ExecutionInterrupted()
context.iterate()
if False:
import cProfile
import pstats
cProfile.runctx("context.iterate()", globals(), locals(),
"profile.prof")
s = pstats.Stats("profile.prof")
s.strip_dirs().sort_stats("time").print_stats()
return df
def simulate_individuals(variables, step_callback=None):
pc = PerfCounter()
age_structure = get_population_for_area().sum(axis=1)
pop_params = dict(
age_structure=age_structure,
contacts_per_day=get_contacts_per_day(),
)
hc_params = dict(hospital_beds=variables['hospital_beds'], icu_units=variables['icu_units'])
disease_params = create_disease_params(variables)
context = model.Context(
population_params=pop_params,
healthcare_params=hc_params,
disease_params=disease_params,
start_date=variables['start_date'],
random_seed=variables['random_seed']
)
start_date = date.fromisoformat(variables['start_date'])
for iv in variables['interventions']:
d = (date.fromisoformat(iv[1]) - start_date).days
if len(iv) > 2:
val = iv[2]
else:
val = 0
context.add_intervention(d, iv[0], val)
pc.measure()
days = variables['simulation_days']
df = pd.DataFrame(
columns=POP_ATTRS + STATE_ATTRS + ['us_per_infected'],
index=pd.date_range(start_date, periods=days)
)
for day in range(days):
s = context.generate_state()
rec = {attr: sum(s[attr]) for attr in POP_ATTRS}
for state_attr in STATE_ATTRS:
rec[state_attr] = s[state_attr]
rec['us_per_infected'] = pc.measure() * 1000 / rec['infected'] if rec['infected'] else 0
if False:
dead = context.get_population_stats('dead')
all_infected = context.get_population_stats('all_infected')
age_groups = pd.interval_range(0, 100, freq=10, closed='left')
s = pd.Series(dead)
dead_by_age = s.groupby(pd.cut(s.index, age_groups)).sum()
dead_by_age.name = 'dead'
s = pd.Series(all_infected)
infected_by_age = s.groupby(pd.cut(s.index, age_groups)).sum()
print(infected_by_age)
zdf = pd.DataFrame(dead_by_age)
zdf['infected'] = infected_by_age
zdf['ifr'] = zdf.dead.divide(zdf.infected.replace(0, np.inf)) * 100
#print(zdf)
d = start_date + timedelta(days=day)
df.loc[d] = rec
if step_callback is not None:
ret = step_callback(df)
if not ret:
raise ExecutionInterrupted()
context.iterate()
if False:
import cProfile
import pstats
cProfile.runctx("context.iterate()", globals(), locals(), "profile.prof")
s = pstats.Stats("profile.prof")
s.strip_dirs().sort_stats("time").print_stats()
return df