def get_nr_of_contacts():
df = get_contacts_for_country()
df = df.drop(columns='place_type').groupby('participant_age').sum()
s = df.sum(axis=1)
idx = list(s.index.map(lambda x: tuple([int(y) for y in x.split('-')])))
s.index = idx
return s.sort_index()
def get_contacts_per_day():
df = get_contacts_for_country()
df = pd.melt(df, id_vars=['place_type', 'participant_age'], var_name='contact_age', value_name='contacts')
df['participant_age'] = df['participant_age'].map(lambda x: tuple([int(y) for y in x.split('-')]))
df['contact_age'] = df['contact_age'].map(lambda x: tuple([int(y) for y in x.split('-')]))
return df
def get_contacts_per_day():
df = get_contacts_for_country()
df = pd.melt(df,
id_vars=['place_type', 'participant_age'],
var_name='contact_age',
value_name='contacts')
df['participant_age'] = df['participant_age'].map(
lambda x: tuple([int(y) for y in x.split('-')]))
df['contact_age'] = df['contact_age'].map(
lambda x: tuple([int(y) for y in x.split('-')]))
df = pd.DataFrame(
[(t.place_type, p, t.contact_age, t.contacts) for t in df.itertuples()
for p in range(t.participant_age[0], t.participant_age[1] + 1)],
columns=['place_type', 'participant_age', 'contact_age', 'contacts'])
# df = pd.DataFrame(
# [(t.place_type, t.participant_age, c, t.contacts / (t.contact_age[1] - t.contact_age[0] + 1)) for t in df.itertuples() for c in range(t.contact_age[0], t.contact_age[1] + 1)],
# columns=['place_type', 'participant_age', 'contact_age', 'contacts']
# )
return df
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):
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