def test_smart_society():
random.seed(42)
o = Outbreak(StrategicTester(),
Covid(),
pop_size=5000,
seed_size=50,
n_days=150)
o.simulate()
def test_two_track_society():
random.seed(42)
o = Outbreak(TwoTrackTester(),
Covid(),
pop_size=5000,
seed_size=50,
n_days=150)
o.simulate()
def test_two_track_hw_society():
random.seed(42)
o = Outbreak(TwoTrackTester(),
Covid(),
pop_size=5000,
seed_size=50,
n_days=150,
population_type=HouseholdWorkplacePopulation)
o.simulate()
def test_two_track_hetero_society():
random.seed(42)
o = Outbreak(TwoTrackTester(),
Covid(),
pop_size=5000,
seed_size=50,
n_days=150,
population_type=RadialAgePopulation)
o.simulate()
def test_two_track_city_society():
random.seed(42)
o = Outbreak(LateralFlowUK(config=dict(SIMULATOR_PERIODS_PER_DAY=4,
DAILY_TEST_CAPACITY_PER_HEAD=1)),
Covid(),
pop_size=8000,
seed_size=8000 // 80,
n_days=150,
population_type=CityPopulation)
o.simulate()
def test_toy_model():
random.seed(42)
s = Society(episodes_per_day=5, encounter_size=2)
d = Disease(days_infectious=10, pr_transmission_per_day=0.2)
# seed size is the number of people in the population who we seed as being infected:
o = Outbreak(s,
d,
pop_size=1000,
seed_size=2,
n_days=ALL_TIME_DAYS,
population_type=Population,
person_type=Person)
# so, this is a village of 10000 people with 2 starting off infected
o.simulate()
np.testing.assert_allclose(o.recorder.story[90:95],
[[18.2, 0.619, 0.57, 0.0, 0.0, 0.0],
[18.4, 0.643, 0.592, 0.0, 0.0, 0.0],
[18.6, 0.656, 0.603, 0.0, 0.0, 0.0],
[18.8, 0.673, 0.618, 0.0, 0.0, 0.0],
[19.0, 0.69, 0.635, 0.0, 0.0, 0.0]])
def test_draconian_population_model():
from codit.society.basic import DraconianSociety
random.seed(42)
s = DraconianSociety(episodes_per_day=5, encounter_size=2)
d = Disease(days_infectious=10, pr_transmission_per_day=0.2)
# seed size is the number of people in the population who we seed as being infected:
o = Outbreak(s,
d,
pop_size=1000,
seed_size=2,
n_days=ALL_TIME_DAYS,
population_type=Population,
person_type=Person)
# so, this is a village of 10000 people with 2 starting off infected
o.simulate()
np.testing.assert_allclose(
o.recorder.story[90:95],
[[18.2, 0.002, 0.0, 0.0, 0.0, 0.0], [18.4, 0.002, 0.0, 0.0, 0.0, 0.0],
[18.6, 0.002, 0.0, 0.0, 0.0, 0.0], [18.8, 0.002, 0.0, 0.0, 0.0, 0.0],
[19.0, 0.002, 0.0, 0.0, 0.0, 0.0]])
def test_covid_model():
s = TestingTracingSociety(episodes_per_day=2,
config=dict(PROB_TEST_IF_REQUESTED=0.4))
random.seed(42)
np.random.seed(42)
o = Outbreak(s, Covid(), pop_size=8, seed_size=1, n_days=ALL_TIME_DAYS)
o.simulate()
# for k, v in o.pop.contacts.items():
# print(k, len(v))
# t cov risks tests isol
np.testing.assert_allclose(o.recorder.story[:15], [
[0.5, 0.25, 0.125, 0.0, 0.125, 0.0],
[1.0, 0.25, 0.125, 0.25, 0.0, 0.0], [1.5, 0.25, 0.125, 0.0, 0.0, 0.0],
[2.0, 0.375, 0.125, 0.0, 0.0, 0.0], [2.5, 0.375, 0.125, 0.0, 0.0, 0.0],
[3.0, 0.375, 0.125, 0.0, 0.0, 0.0], [3.5, 0.375, 0.125, 0.0, 0.0, 0.0],
[4.0, 0.375, 0.125, 0.0, 0.0, 0.125],
[4.5, 0.375, 0.25, 0.0, 0.0, 0.125], [5.0, 0.5, 0.25, 0.0, 0.0, 0.125],
[5.5, 0.5, 0.125, 0.0, 0.0, 0.125], [6.0, 0.5, 0.25, 0.0, 0.0, 0.125],
[6.5, 0.5, 0.25, 0.0, 0.0, 0.125], [7.0, 0.5, 0.25, 0.0, 0.0, 0.125],
[7.5, 0.5, 0.25, 0.0, 0.0, 0.125]
])
def test_uk_ovespill_model():
from codit.society.alternatives import UKSociety
from codit.disease import Covid
random.seed(42)
np.random.seed(42)
o = Outbreak(UKSociety(config=dict(PROB_NON_C19_SYMPTOMS_PER_DAY=0.1)),
Covid(),
pop_size=1000,
seed_size=20,
n_days=ALL_TIME_DAYS)
o.simulate()
# Skip this test because the the simulator is not deterministic.
# I believe it is because it uses np.random by calls multi-threaded numpy code.
# TODO: I believe the correct solution is for the simulator to be constructed with its own np.random.RandomState class
#
return
np.testing.assert_allclose(
o.recorder.story[40:45] == [[41.0, 0.035, 0.0, 0.007, 0.467, 0.452],
[42.0, 0.035, 0.0, 0.007, 0.458, 0.442],
[43.0, 0.035, 0.0, 0.007, 0.461, 0.447],
[44.0, 0.035, 0.0, 0.007, 0.458, 0.448],
[45.0, 0.035, 0.0, 0.007, 0.457, 0.448]])
def test_covid_model():
from codit.society import TestingSociety
from codit.disease import Covid
s = TestingSociety(episodes_per_day=5,
config={
"MEAN_NETWORK_SIZE": 2,
"PROB_NON_C19_SYMPTOMS_PER_DAY": 0,
"PROB_TEST_IF_REQUESTED": 0.4
})
d = Covid(days_infectious=10, pr_transmission_per_day=0.2)
# seed size is the number of people in the population who we seed as being infected:
random.seed(42)
o = Outbreak(s, d, pop_size=1000, seed_size=20, n_days=ALL_TIME_DAYS)
# so, this is a village of 10000 people with 2 starting off infected
random.seed(42)
o.simulate()
np.testing.assert_allclose(o.recorder.story[90:95],
[[18.2, 0.103, 0.044, 0.0, 0.0, 0.023],
[18.4, 0.103, 0.046, 0.0, 0.0, 0.023],
[18.6, 0.103, 0.045, 0.0, 0.0, 0.021],
[18.8, 0.105, 0.043, 0.0, 0.0, 0.019],
[19.0, 0.105, 0.043, 0.0, 0.0, 0.017]])
def test_two_track_model():
s = TwoTrackTester(episodes_per_day=2)
random.seed(42)
np.random.seed(42)
o = Outbreak(s, Covid(), pop_size=8, seed_size=1, n_days=ALL_TIME_DAYS)
o.simulate()
# for k, v in o.pop.contacts.items():
# print(k, len(v))
# t cov risks tests tests_back isol
np.testing.assert_allclose(o.recorder.story[:15], [[0.5, 0.25, 0.125, 0.0, 0.125, 0.0],
[1.0, 0.25, 0.125, 0.0, 0.125, 0.0],
[1.5, 0.25, 0.125, 0.0, 0.125, 0.0],
[2.0, 0.25, 0.125, 0.0, 0.125, 0.0],
[2.5, 0.25, 0.125, 0.0, 0.125, 0.0],
[3.0, 0.25, 0.125, 0.0, 0.125, 0.0],
[3.5, 0.25, 0.125, 0.0, 0.125, 0.0],
[4.0, 0.375, 0.125, 0.0, 0.125, 0.0],
[4.5, 0.375, 0.25, 0.0, 0.125, 0.0],
[5.0, 0.5, 0.25, 0.0, 0.125, 0.0],
[5.5, 0.5, 0.125, 0.0, 0.125, 0.0],
[6.0, 0.5, 0.125, 0.0, 0.125, 0.0],
[6.5, 0.5, 0.125, 0.0, 0.125, 0.0],
[7.0, 0.5, 0.125, 0.0, 0.125, 0.0],
[7.5, 0.5, 0.125, 0.0, 0.125, 0.0]])