def test_preexisting_conditions(self):
"""
Test the distribution of the preexisting conditions
"""
def _get_id(cond):
return PREEXISTING_CONDITIONS[cond][0].id
def _get_probability(cond_probs, age, sex):
if isinstance(cond_probs, str):
cond_probs = PREEXISTING_CONDITIONS[cond_probs]
return next((c_prob.probability for c_prob in cond_probs
if age < c_prob.age and (c_prob.sex in ('a', sex))))
n_people = 10000
rng = np.random.RandomState(1234)
c_ids = set()
for c_name, c_probs in PREEXISTING_CONDITIONS.items():
c_id = c_probs[0].id
self.assertNotIn(c_id, c_ids)
c_ids.add(c_id)
# TODO: Implement test for stroke and pregnant
if c_id in (_get_id('stroke'), _get_id('pregnant')):
continue
for c_prob in c_probs:
self.assertEqual(c_prob.id, c_probs[0].id)
age, sex = c_prob.age - 1, c_prob.sex
expected_prob = c_prob.probability
if c_id in (_get_id('cancer'), _get_id('COPD')):
modifer_prob = _get_probability('smoker', age, sex)
expected_prob = expected_prob * (1.3 * modifer_prob + 0.95 * (1 - modifer_prob))
if c_id == _get_id('heart_disease'):
modifer_prob = _get_probability('diabetes', age, sex) + \
_get_probability('smoker', age, sex)
expected_prob = expected_prob * (2 * modifer_prob + 0.5 * (1 - modifer_prob))
if c_id == _get_id('immuno-suppressed'):
modifer_prob = _get_probability('cancer', age, sex)
expected_prob = expected_prob * (1.2 * modifer_prob + 0.98 * (1 - modifer_prob))
if c_id == _get_id('lung_disease'):
expected_prob = _get_probability('asthma', age, sex) + \
_get_probability('COPD', age, sex)
computed_dist = [_get_preexisting_conditions(age, sex, rng) for _ in range(n_people)]
prob = len([1 for conditions in computed_dist if c_name in conditions]) / n_people
self.assertAlmostEqual(prob, expected_prob,
delta=0 if not expected_prob else max(0.015, expected_prob * 0.05),
msg=f"Computation of the preexisting conditions [{c_name}] yielded an "
f"unexpected probability for age {age} and sex {sex}")
def test_preexisting_conditions(self):
"""
Test the distribution of the preexisting conditions
"""
n_people = 10000
rng = np.random.RandomState(1234)
for c_name, c_prob in PREEXISTING_CONDITIONS.items():
for p in c_prob:
computed_dist = [
_get_preexisting_conditions(p.age - 1, p.sex, rng)
for _ in range(n_people)
]
prob = len([cd for cd in computed_dist if p.name in cd
]) / n_people
self.assertTrue(
abs(p.probability - prob) < 0.01,
msg=f"Computation of the preexisting conditions"
f"yielded a different probability than expected"
f"at {prob} instead of {p.probability}")
def __init__(self, env, name, age, rng, infection_timestamp, household, workplace, profession, rho=0.3, gamma=0.21, symptoms=[],
test_results=None):
self.last_date = env.timestamp.date()
self.env = env
self._events = []
self.name = f"human:{name}"
self.rng = rng
self.profession = profession
self.is_healthcare_worker = True if profession == "healthcare" else False
self.assign_household(household)
self.workplace = workplace
self.rho = rho
self.gamma = gamma
self.age = age
self.sex = _get_random_sex(self.rng)
self.preexisting_conditions = _get_preexisting_conditions(self.age, self.sex, self.rng)
age_modifier = 2 if self.age > 40 or self.age < 12 else 2
# &carefulness
if self.rng.rand() < P_CAREFUL_PERSON:
self.carefulness = (round(self.rng.normal(55, 10)) + self.age/2) / 100
else:
self.carefulness = (round(self.rng.normal(25, 10)) + self.age/2) / 100
self.has_app = self.rng.rand() < (P_HAS_APP / age_modifier) + (self.carefulness / 2)
# logged info can be quite different
self.has_logged_info = self.has_app and self.rng.rand() < self.carefulness
self.obs_is_healthcare_worker = True if self.is_healthcare_worker and rng.random()<0.9 else False # 90% of the time, healthcare workers will declare it
self.obs_age = self.age if self.has_app and self.has_logged_info else None
self.obs_sex = self.sex if self.has_app and self.has_logged_info else None
self.obs_preexisting_conditions = self.preexisting_conditions if self.has_app and self.has_logged_info else None
self.rest_at_home = False # to track mobility due to symptoms
self.visits = Visits()
self.travelled_recently = self.rng.rand() > 0.9
# &symptoms, &viral-load
# probability of being asymptomatic is basically 50%, but a bit less if you're older and a bit more if you're younger
self.is_asymptomatic = self.rng.rand() < (BASELINE_P_ASYMPTOMATIC - (self.age - 50) * 0.5) / 100
self.asymptomatic_infection_ratio = ASYMPTOMATIC_INFECTION_RATIO if self.is_asymptomatic else 0.0 # draw a beta with the distribution in documents
self.viral_load_plateau_height, self.viral_load_plateau_start, self.viral_load_plateau_end, self.viral_load_recovered = _sample_viral_load_piecewise(rng, age=age)
self.infectiousness_onset_days = self.rng.normal(loc=INFECTIOUSNESS_ONSET_DAYS_AVG, scale=INFECTIOUSNESS_ONSET_DAYS_STD)
self.incubation_days = self.infectiousness_onset_days + self.viral_load_plateau_start + self.rng.normal(loc=SYMPTOM_ONSET_WRT_VIRAL_LOAD_PEAK_AVG, scale=SYMPTOM_ONSET_WRT_VIRAL_LOAD_PEAK_STD)
self.recovery_days = self.infectiousness_onset_days + self.viral_load_recovered
self.test_result, self.test_type = None, None
# Indicates whether this person will show severe signs of illness.
self.infection_timestamp = infection_timestamp
self.cold_timestamp = self.env.timestamp if self.rng.random() < P_COLD else None
self.flu_timestamp = self.env.timestamp if self.rng.random() < P_FLU else None # different from asymptomatic
self.recovered_timestamp = datetime.datetime.min
self.is_immune = False # different from asymptomatic
self.can_get_really_sick = self.rng.random() >= 0.8 + (age/100)
self.can_get_extremely_sick = self.can_get_really_sick and self.rng.random() >= 0.7 # &severe; 30% of severe cases need ICU
self.never_recovers = self.rng.random() <= P_NEVER_RECOVERS[min(math.floor(self.age/10),8)]
self.obs_hospitalized = False
self.obs_in_icu = False
# counters and memory
self.r0 = []
self.has_logged_symptoms = False
self.has_logged_test = False
self.last_state = self.state
self.n_infectious_contacts = 0
self.last_date_to_check_symptoms = self.env.timestamp.date()
# symptoms
self.symptom_start_time = None
self.all_cold_symptoms = _get_cold_symptoms_v2(self.age, self.rng, self.carefulness, self.preexisting_conditions, self.can_get_really_sick, self.can_get_extremely_sick)
self.all_flu_symptoms = _get_flu_symptoms_v2(self.age, self.rng, self.carefulness, self.preexisting_conditions, self.can_get_really_sick, self.can_get_extremely_sick)
self.all_covid_symptoms = _get_covid_symptoms(self.viral_load_plateau_start, self.viral_load_plateau_end,
self.viral_load_recovered, age=self.age, incubation_days=self.incubation_days,
really_sick=self.can_get_really_sick, extremely_sick=self.can_get_extremely_sick,
rng=self.rng, preexisting_conditions=self.preexisting_conditions)
self.all_symptoms, self.cold_symptoms, self.flu_symptoms, self.covid_symptoms = [], [], [], []
# habits
self.avg_shopping_time = _draw_random_discreet_gaussian(AVG_SHOP_TIME_MINUTES, SCALE_SHOP_TIME_MINUTES, self.rng)
self.scale_shopping_time = _draw_random_discreet_gaussian(AVG_SCALE_SHOP_TIME_MINUTES,
SCALE_SCALE_SHOP_TIME_MINUTES, self.rng)
self.avg_exercise_time = _draw_random_discreet_gaussian(AVG_EXERCISE_MINUTES, SCALE_EXERCISE_MINUTES, self.rng)
self.scale_exercise_time = _draw_random_discreet_gaussian(AVG_SCALE_EXERCISE_MINUTES,
SCALE_SCALE_EXERCISE_MINUTES, self.rng)
self.avg_working_minutes = _draw_random_discreet_gaussian(AVG_WORKING_MINUTES, SCALE_WORKING_MINUTES, self.rng)
self.scale_working_minutes = _draw_random_discreet_gaussian(AVG_SCALE_WORKING_MINUTES, SCALE_SCALE_WORKING_MINUTES, self.rng)
self.avg_hospital_hours = _draw_random_discreet_gaussian(AVG_HOSPITAL_HOURS, SCALE_HOSPITAL_HOURS, self.rng)
self.scale_hospital_hours = _draw_random_discreet_gaussian(AVG_SCALE_HOSPITAL_HOURS, SCALE_SCALE_HOSPITAL_HOURS, self.rng)
self.avg_misc_time = _draw_random_discreet_gaussian(AVG_MISC_MINUTES, SCALE_MISC_MINUTES, self.rng)
self.scale_misc_time = _draw_random_discreet_gaussian(AVG_SCALE_MISC_MINUTES, SCALE_SCALE_MISC_MINUTES, self.rng)
#getting the number of shopping days and hours from a distribution
self.number_of_shopping_days = _draw_random_discreet_gaussian(AVG_NUM_SHOPPING_DAYS, SCALE_NUM_SHOPPING_DAYS, self.rng)
self.number_of_shopping_hours = _draw_random_discreet_gaussian(AVG_NUM_SHOPPING_HOURS, SCALE_NUM_SHOPPING_HOURS, self.rng)
#getting the number of exercise days and hours from a distribution
self.number_of_exercise_days = _draw_random_discreet_gaussian(AVG_NUM_EXERCISE_DAYS, SCALE_NUM_EXERCISE_DAYS, self.rng)
self.number_of_exercise_hours = _draw_random_discreet_gaussian(AVG_NUM_EXERCISE_HOURS, SCALE_NUM_EXERCISE_HOURS, self.rng)
#Multiple shopping days and hours
self.shopping_days = self.rng.choice(range(7), self.number_of_shopping_days)
self.shopping_hours = self.rng.choice(range(7, 20), self.number_of_shopping_hours)
#Multiple exercise days and hours
self.exercise_days = self.rng.choice(range(7), self.number_of_exercise_days)
self.exercise_hours = self.rng.choice(range(7, 20), self.number_of_exercise_hours)
#Limiting the number of hours spent shopping per week
self.max_shop_per_week = _draw_random_discreet_gaussian(AVG_MAX_NUM_SHOP_PER_WEEK, SCALE_MAX_NUM_SHOP_PER_WEEK, self.rng)
self.count_shop=0
# Limiting the number of hours spent exercising per week
self.max_exercise_per_week = _draw_random_discreet_gaussian(AVG_MAX_NUM_EXERCISE_PER_WEEK, SCALE_MAX_NUM_EXERCISE_PER_WEEK, self.rng)
self.count_exercise=0
self.work_start_hour = self.rng.choice(range(7, 12), 3)
def __init__(self,
env,
name,
age,
rng,
infection_timestamp,
household,
workplace,
rho=0.3,
gamma=0.21,
symptoms=[],
test_results=None):
self.env = env
self.events = []
self.name = name
self.rng = rng
self.age = _get_random_age(self.rng)
self.sex = _get_random_sex(self.rng)
self.preexisting_conditions = _get_preexisting_conditions(
self.age, self.sex, self.rng)
self.household = household
self.workplace = workplace
self.location = household
self.rho = rho
self.gamma = gamma
self.visits = Visits()
self.travelled_recently = self.rng.rand() > 0.9
# &carefullness
if self.rng.rand() < P_CAREFUL_PERSON:
self.carefullness = round(self.rng.normal(55, 10)) + self.age / 2
else:
self.carefullness = round(self.rng.normal(25, 10)) + self.age / 2
age_modifier = 1
if self.age > 40 or self.age < 12:
age_modifier = 2
self.has_cold = self.rng.rand() < P_COLD * age_modifier
self.has_flu = self.rng.rand() < P_FLU * age_modifier
self.has_app = self.rng.rand() < (P_HAS_APP / age_modifier) + (
self.carefullness / 2)
self.incubation_days = _draw_random_discreet_gaussian(
AVG_INCUBATION_DAYS, SCALE_INCUBATION_DAYS, self.rng)
# Indicates whether this person will show severe signs of illness.
self.infection_timestamp = infection_timestamp
self.recovered_timestamp = datetime.datetime.min
self.really_sick = self.is_exposed and self.rng.random() >= 0.9
self.extremely_sick = self.really_sick and self.rng.random(
) >= 0.7 # &severe; 30% of severe cases need ICU
self.never_recovers = self.rng.random() >= 0.99
# &symptoms, &viral-load
# probability of being asymptomatic is basically 50%, but a bit less if you're older
# and a bit more if you're younger
self.is_asymptomatic = self.rng.rand() > (BASELINE_P_ASYMPTOMATIC -
(self.age - 50) * 0.5) / 100
self.asymptomatic_infection_ratio = 0.0
if self.is_asymptomatic:
self.asymptomatic_infection_ratio = ASYMPTOMATIC_INFECTION_RATIO # draw a beta with the distribution in documents
self.recovery_days = _draw_random_discreet_gaussian(
AVG_RECOVERY_DAYS, SCALE_RECOVERY_DAYS,
self.rng) # make it IQR &recovery
self.viral_load_plateau_height, self.viral_load_plateau_start, self.viral_load_plateau_end, self.viral_load_recovered = _sample_viral_load_piecewise(
rng, age=age)
self.all_symptoms_array = _get_all_symptoms_array(
np.ndarray.item(self.viral_load_plateau_start),
np.ndarray.item(self.viral_load_plateau_end),
np.ndarray.item(self.viral_load_recovered),
age=self.age,
incubation_days=self.incubation_days,
really_sick=self.really_sick,
extremely_sick=self.extremely_sick,
rng=self.rng,
preexisting_conditions=self.preexisting_conditions)
# counters and memory
self.r0 = []
self.has_logged_symptoms = False
self.has_logged_test = False
self.n_infectious_contacts = 0
self.last_state = self.state
# habits
self.avg_shopping_time = _draw_random_discreet_gaussian(
AVG_SHOP_TIME_MINUTES, SCALE_SHOP_TIME_MINUTES, self.rng)
self.scale_shopping_time = _draw_random_discreet_gaussian(
AVG_SCALE_SHOP_TIME_MINUTES, SCALE_SCALE_SHOP_TIME_MINUTES,
self.rng)
self.avg_exercise_time = _draw_random_discreet_gaussian(
AVG_EXERCISE_MINUTES, SCALE_EXERCISE_MINUTES, self.rng)
self.scale_exercise_time = _draw_random_discreet_gaussian(
AVG_SCALE_EXERCISE_MINUTES, SCALE_SCALE_EXERCISE_MINUTES, self.rng)
self.avg_working_hours = _draw_random_discreet_gaussian(
AVG_WORKING_MINUTES, SCALE_WORKING_MINUTES, self.rng)
self.scale_working_hours = _draw_random_discreet_gaussian(
AVG_SCALE_WORKING_MINUTES, SCALE_SCALE_WORKING_MINUTES, self.rng)
self.avg_misc_time = _draw_random_discreet_gaussian(
AVG_MISC_MINUTES, SCALE_MISC_MINUTES, self.rng)
self.scale_misc_time = _draw_random_discreet_gaussian(
AVG_SCALE_MISC_MINUTES, SCALE_SCALE_MISC_MINUTES, self.rng)
#getting the number of shopping days and hours from a distribution
self.number_of_shopping_days = _draw_random_discreet_gaussian(
AVG_NUM_SHOPPING_DAYS, SCALE_NUM_SHOPPING_DAYS, self.rng)
self.number_of_shopping_hours = _draw_random_discreet_gaussian(
AVG_NUM_SHOPPING_HOURS, SCALE_NUM_SHOPPING_HOURS, self.rng)
#getting the number of exercise days and hours from a distribution
self.number_of_exercise_days = _draw_random_discreet_gaussian(
AVG_NUM_EXERCISE_DAYS, SCALE_NUM_EXERCISE_DAYS, self.rng)
self.number_of_exercise_hours = _draw_random_discreet_gaussian(
AVG_NUM_EXERCISE_HOURS, SCALE_NUM_EXERCISE_HOURS, self.rng)
#Multiple shopping days and hours
self.shopping_days = self.rng.choice(range(7),
self.number_of_shopping_days)
self.shopping_hours = self.rng.choice(range(7, 20),
self.number_of_shopping_hours)
#Multiple exercise days and hours
self.exercise_days = self.rng.choice(range(7),
self.number_of_exercise_days)
self.exercise_hours = self.rng.choice(range(7, 20),
self.number_of_exercise_hours)
#Limiting the number of hours spent shopping per week
self.max_shop_per_week = _draw_random_discreet_gaussian(
AVG_MAX_NUM_SHOP_PER_WEEK, SCALE_MAX_NUM_SHOP_PER_WEEK, self.rng)
self.count_shop = 0
#Limiting the number of hours spent exercising per week
self.max_exercise_per_week = _draw_random_discreet_gaussian(
AVG_MAX_NUM_EXERCISE_PER_WEEK, SCALE_MAX_NUM_EXERCISE_PER_WEEK,
self.rng)
self.count_exercise = 0
self.work_start_hour = self.rng.choice(range(7, 12))