def cache_populations(seed=0, popfile=None):
''' Pre-generate the synthpops population '''
pars = sc.objdict(
pop_size=pop_size,
pop_type='synthpops',
rand_seed=seed,
)
if popfile is None:
popfile = f'{popfile_stem}{pars.rand_seed}.ppl'
T = sc.tic()
print(f'Making "{popfile}"...')
sim = cv.Sim(pars)
cv.make_people(sim,
popfile=popfile,
save_pop=True,
with_facilities=True,
generate=True,
layer_mapping={'LTCF': 'l'})
sc.toc(T)
print('Done')
return
def plot_people(self, *args, **kwargs):
''' Placeholder example of plotting the people in a population '''
import covasim as cv # Optional import
pars = dict(
pop_size = self.n,
pop_type = 'synthpops',
beta_layer = {k: 1 for k in 'hscwl'},
)
sim = cv.Sim(pars, popfile=self.popdict)
ppl = cv.make_people(sim) # Create the corresponding population
fig = ppl.plot(*args, **kwargs)
return fig
def cache_populations(seed=0, popfile=None):
''' Pre-generate the hybrid population '''
pars = sc.objdict(
pop_size=225e3,
pop_type='hybrid',
rand_seed=seed,
)
if popfile is None:
popfile = f'inputs/kc_hybrid_seed{pars.rand_seed}.ppl'
T = sc.tic()
print(f'Making "{popfile}"...')
sim = cv.Sim(pars)
cv.make_people(sim,
popfile=popfile,
save_pop=True,
school_type=True,
school_type_ages=[[6, 11], [11, 14], [14, 18], [18, 22]])
sc.toc(T)
print('Done')
return
location = 'seattle_metro'
undirected = True
n_days = 1
rand_seed = None
with_facilities = False
pars = {
'pop_size': pop_size,
'pop_type': pop_type,
'rand_seed': rand_seed,
'n_days': n_days,
}
sim = cv.Sim(pars)
popdict = cv.make_people(sim,
generate=True,
with_facilities=with_facilities,
layer_mapping={'LTCF': 'l'})
sim = cv.Sim(pars, popfile=popdict, load_pop=True)
# Select which keys to plot!
# keys_to_plot = ['h', 'l']
# keys_to_plot = ['s', 'w']
keys_to_plot = ['h', 'l', 's', 'w']
# keys_to_plot = ['h', 's', 'w']
keys = ['l', 'w', 's', 'h']
# keys = ['w', 's', 'h']
if with_facilities is False:
if 'l' in keys_to_plot:
keys_to_plot.remove('l')
prognoses['symp_probs'] = np.array(
[0.905, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0]) # Equally susceptible
prognoses['severe_probs'] = np.array(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) # Zero'd out
prognoses['crit_probs'] = np.array(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) # Zero'd out
prognoses['death_probs'] = np.array(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) # Zero'd out
pars['prognoses'] = prognoses
pars['start_day'] = '2020-01-01'
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()
def make_population(seed=0, popfile=None):
''' Pre-generate the synthpops population '''
pars = sc.objdict(
pop_size=10e3,
pop_type='synthpops',
rand_seed=seed,
)
use_two_group_reduction = True
average_LTCF_degree = 20
ltcf_staff_age_min = 20
ltcf_staff_age_max = 60
with_school_types = True
average_class_size = 20
inter_grade_mixing = 0.1
average_student_teacher_ratio = 20
average_teacher_teacher_degree = 3
teacher_age_min = 25
teacher_age_max = 75
with_non_teaching_staff = True
# if with_non_teaching_staff is False, but generate is True, then average_all_staff_ratio should be average_student_teacher_ratio or 0
average_student_all_staff_ratio = 11
average_additional_staff_degree = 20
staff_age_min = 20
staff_age_max = 75
school_mixing_type = {
'pk': 'clustered',
'es': 'clustered',
'ms': 'clustered',
'hs': 'random',
'uv': 'random'
}
if popfile is None:
popfile = f'inputs/kc_synthpops_clustered_withstaff_10e3.ppl'
T = sc.tic()
print(f'Making "{popfile}"...')
sim = cv.Sim(pars)
cv.make_people(
sim,
popfile=popfile,
save_pop=True,
generate=True,
with_facilities=True,
use_two_group_reduction=use_two_group_reduction,
average_LTCF_degree=average_LTCF_degree,
ltcf_staff_age_min=ltcf_staff_age_min,
ltcf_staff_age_max=ltcf_staff_age_max,
with_school_types=with_school_types,
school_mixing_type=school_mixing_type,
average_class_size=average_class_size,
inter_grade_mixing=inter_grade_mixing,
average_student_teacher_ratio=average_student_teacher_ratio,
average_teacher_teacher_degree=average_teacher_teacher_degree,
teacher_age_min=teacher_age_min,
teacher_age_max=teacher_age_max,
with_non_teaching_staff=with_non_teaching_staff,
average_student_all_staff_ratio=average_student_all_staff_ratio,
average_additional_staff_degree=average_additional_staff_degree,
staff_age_min=staff_age_min,
staff_age_max=staff_age_max)
sc.toc(T)
print('Done')
return
reportdir = pathlib.Path(os.path.dirname(__file__), "covasim_report")
os.makedirs(reportdir, exist_ok=True)
n = 2e4 + 1 # Total population size
for seed in range(1, 500, 100):
for ltcf in [True, False]:
label = "ltcf_" if ltcf else ""
print("seed:", seed) # Random seed
# Everything here gets passed to sp.make_population()
pop_pars = dict(
generate=True,
with_facilities=ltcf,
)
if ltcf:
pop_pars["layer_mapping"] = {'LTCF': 'l'}
sim = cv.Sim(pop_size=n,
rand_seed=seed,
pop_type='synthpops',
beta_layer={k: 1
for k in 'hscwl'})
else:
sim = cv.Sim(pop_size=n, rand_seed=seed,
pop_type='synthpops') # Make the Covasim oject
ppl = cv.make_people(sim,
**pop_pars) # Create the corresponding population
fig = ppl.plot()
fig.savefig(os.path.join(
reportdir, f"{label}covasim_graph_{n}_seed{seed}_new.png"),
format="png")
def cache_populations(seed=0, popfile=None):
''' Pre-generate the synthpops population '''
pars = sc.objdict(
# pop_size = 2.25e6,
pop_size=225e3,
pop_type='synthpops',
rand_seed=seed,
)
use_two_group_reduction = True
average_LTCF_degree = 20
ltcf_staff_age_min = 20
ltcf_staff_age_max = 60
with_school_types = True
average_class_size = 20
inter_grade_mixing = 0.1
average_student_teacher_ratio = 20
average_teacher_teacher_degree = 3
teacher_age_min = 25
teacher_age_max = 75
with_non_teaching_staff = True
# if with_non_teaching_staff is False, but generate is True, then average_all_staff_ratio should be average_student_teacher_ratio or 0
average_student_all_staff_ratio = 11
average_additional_staff_degree = 20
staff_age_min = 20
staff_age_max = 75
# For reference re: school_types
# school_mixing_type = 'random' means that students in the school have edges randomly chosen from other students, teachers, and non teaching staff across the school. Students, teachers, and non teaching staff are treated the same in terms of edge generation.
# school_mixing_type = 'age_clustered' means that students in the school have edges mostly within their own age/grade, with teachers, and non teaching staff. Strict classrooms are not generated. Teachers have some additional edges with other teachers.
# school_mixing_type = 'age_and_class_clustered' means that students are cohorted into classes of students of the same age/grade with at least 1 teacher, and then some have contact with non teaching staff. Teachers have some additional edges with other teachers.
cohorting = False
if cohorting:
strategy = 'clustered' # students in pre-k, elementary, and middle school are cohorted into strict classrooms
school_mixing_type = {
'pk': 'age_and_class_clustered',
'es': 'age_and_class_clustered',
'ms': 'age_and_class_clustered',
'hs': 'random',
'uv': 'random'
}
else:
strategy = 'normal'
school_mixing_type = {
'pk': 'age_clustered',
'es': 'age_clustered',
'ms': 'age_clustered',
'hs': 'random',
'uv': 'random'
}
if popfile is None:
popfile = f'inputs/kc_synthpops_{strategy}_withstaff_seed{pars.rand_seed}.ppl'
# popfile = f'inputs/kc_synthpops_{strategy}_withstaff_1m_seed{pars.rand_seed}.ppl'
T = sc.tic()
print(f'Making "{popfile}"...')
sim = cv.Sim(pars)
cv.make_people(
sim,
popfile=popfile,
save_pop=True,
generate=True,
with_facilities=True,
use_two_group_reduction=use_two_group_reduction,
average_LTCF_degree=average_LTCF_degree,
ltcf_staff_age_min=ltcf_staff_age_min,
ltcf_staff_age_max=ltcf_staff_age_max,
with_school_types=with_school_types,
school_mixing_type=school_mixing_type,
average_class_size=average_class_size,
inter_grade_mixing=inter_grade_mixing,
average_student_teacher_ratio=average_student_teacher_ratio,
average_teacher_teacher_degree=average_teacher_teacher_degree,
teacher_age_min=teacher_age_min,
teacher_age_max=teacher_age_max,
with_non_teaching_staff=with_non_teaching_staff,
average_student_all_staff_ratio=average_student_all_staff_ratio,
average_additional_staff_degree=average_additional_staff_degree,
staff_age_min=staff_age_min,
staff_age_max=staff_age_max)
sc.toc(T)
print('Done')
return
sc.toc(label='default') plist = people.to_people() sc.toc(label='to people') ppl2 = cv.People() ppl2.from_people(plist) sc.toc(label='from people') ppl3 = people + ppl2 sim = cv.Sim(pop_type='random', pop_size=20000) cv.make_people(sim) ppl4 = sim.people sc.toc(label='as sim') df = ppl4.to_df() arr = ppl4.to_arr() sc.toc(label='to df/arr') sc.toc() sim.people.initialize() df = sim.people.contacts