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build_contact_trees.py
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import load_contact_data
import numpy as np
import numpy.random
import scipy
import scipy.stats
import load_contact_data
import fit_dist
import code
import matplotlib
import matplotlib.pyplot as plt
from numba import jit, njit
from numba.typed import List
from numba import prange
import numba
from multiprocessing import Pool
import math
import random
import scipy.optimize
import json
age_intervals = [(0, 14), (15, 24), (25, 54), (55, 64), (65, np.inf)]
age_specific_IFRs = np.array([0.0001, 0.0001, 0.0013, 0.007, 0.068])
age_vector_US = np.array([0.1862, 0.1312, 0.3929, 0.1294, 0.1603])
age_vector_US = age_vector_US / np.sum(age_vector_US)
false_positive_rate = 0.0
frac_asymptoms = 0.20
asymp_infectiousness = 0.35
presymp_infectiousness = 0.63
""" relative transmission rate of asymptoms individual """
days_infectious_before_symptoms = 2
days_infectious_after_symptoms = 5
total_infectious_days = days_infectious_before_symptoms + days_infectious_after_symptoms
asymp_quarantine_length = 14
symp_quarantine_length = 10
symptoms_household_SAR = 0.2
symptoms_external_SAR = 0.06
superspreader_SAR_ratio = 10
# frac_infs_from_SS = 0.9
index_R0 = 2.548934288671604
raw_R0 = 2.5
# raw_R0 = 1.48 # 50% reduction
quarantine_dropout_rate_default = 0.05
length_of_sympts_to_track = 30
symptom_false_positive_chance = 0.01
pooling_max = 20
dropout_reduction_for_symptoms = 0.5
frac_self_isolate = 0.9
frac_SS_cal = 1 / (1 + np.exp(2.1))
base_household_cal = 1 / (1 + np.exp(3.3))
base_external_cal = 1 / (1 + np.exp(5.))
SS_mult_cal = 1 + np.log(1 + np.exp(23.4))
phys_mult_cal = 1 + np.log(1 + np.exp(4.2))
fraction_household_physical = [0.] # filled in by get_contacts_per_age
fraction_external_physical = [0.] # filled in by get_contacts_per_age
n_processes = 1
def distancing_from_R0_squared(R0_squared_obs):
# return 1 - math.sqrt(1 / R0_squared_obs)
return 1 - raw_R0 / R0_squared_obs
def reduction_from_R0_squared(R0_squared_obs):
return 1 - (R0_squared_obs / index_R0) / raw_R0
""" convert raw numeric age into age interval """
def raw_age_to_interval(age, age_intervals):
for i in range(len(age_intervals)):
if age >= age_intervals[i][0] and age <= age_intervals[i][1]:
return i
""" process POLYMOD data into dict of contacts by age """
def get_contacts_per_age():
total_household = 0
total_external = 0
age_to_id_dict = {}
age_to_contacts_dict = {}
for i in range(len(age_intervals)):
age_to_contacts_dict[i] = List()
age_i = np.logical_and(load_contact_data.raw_participants["part_age"]
>= age_intervals[i][0], load_contact_data.raw_participants["part_age"] <= age_intervals[i][1])
age_to_id_dict[i] = list(load_contact_data.raw_participants[age_i]['part_id'])
for j in age_to_id_dict[i]:
contacts_j = load_contact_data.raw_contacts[load_contact_data.raw_contacts['part_id'] == j]
contacts = List()
contacts.append((1, True, True, True, True))
contacts.pop()
""" contact format: (age of contact, gender, is_household, is_daily, duration, is_physical) """
contacts_numpy = contacts_j.to_numpy()
for row in range(np.shape(contacts_numpy)[0]):
if contacts_numpy[row][14] <= 2:
continue
# assert(contacts_numpy[row][5] == 'F' or contacts_numpy[row][5] == 'M')
try:
contacts.append((raw_age_to_interval(int(contacts_numpy[row][2]), age_intervals),
contacts_numpy[row][5] == 'F',
bool(contacts_numpy[row][6]),
int(contacts_numpy[row][12]) == 1,
int(contacts_numpy[row][13]) == 1))
if bool(contacts_numpy[row][6]):
total_household += 1
if int(contacts_numpy[row][12]) == 1:
fraction_household_physical[0] += 1
else:
total_external += 1
if int(contacts_numpy[row][12]) == 1:
fraction_external_physical[0] += 1
except Exception:
pass
age_to_contacts_dict[i].append(contacts)
fraction_external_physical[0] = fraction_external_physical[0] / total_external
fraction_household_physical[0] = fraction_household_physical[0] / total_household
return age_to_contacts_dict
# @njit
# def true_positive_test_rate(test_time_rel_to_symptoms):
# if test_time_rel_to_symptoms <= -3:
# return 0.0
# if test_time_rel_to_symptoms == -2:
# return 0.0
# if test_time_rel_to_symptoms == -1:
# return 0.0
# if test_time_rel_to_symptoms == 0:
# return 0.0
# else:
# return 0.0
@njit
def false_negative_test_rate(test_time_rel_to_symptoms):
if test_time_rel_to_symptoms <= -3:
return 1.0
if test_time_rel_to_symptoms == -2:
return 0.95
if test_time_rel_to_symptoms == -1:
return 0.7
if test_time_rel_to_symptoms == 0:
return 0.4
if test_time_rel_to_symptoms <= 4:
return 0.25
else:
return min(0.25 + (test_time_rel_to_symptoms - 4) * 0.0375, 1.)
@jit(nopython=True, parallel=True)
def calc_test_results(num_individuals, test_time_rel_to_symptoms, I_COVID, seed=0):
test_results = np.zeros(num_individuals)
for i in prange(num_individuals):
test_results[i] = calc_test_single(test_time_rel_to_symptoms[i], I_COVID[i])
return test_results
@njit
def calc_test_single(test_time_rel_to_symptoms, I_COVID):
if I_COVID:
return np.random.binomial(n=1, p=1 - false_negative_test_rate(int(round(test_time_rel_to_symptoms))))
else:
return 0
""" draw properties of seed index cases """
def draw_seed_index_cases(num_individuals, age_vector, cases_contacted=1.0,
t_exposure=None, fpr=false_positive_rate, initial=False, skip_days=False,
random_testing=False, test_freq=0., test_delay=0.,
frac_SS=frac_SS_cal, frac_vacc=0., seed=0):
np.random.seed(seed)
if t_exposure is None:
t_exposure = np.zeros(num_individuals)
params = {}
n_ages = scipy.stats.multinomial.rvs(num_individuals, age_vector)
if t_exposure is None:
params['t_exposure'] = np.zeros(num_individuals)
else:
params['t_exposure'] = t_exposure
params['t_last_exposure'] = params['t_exposure']
params['I_contacted'] = scipy.stats.bernoulli.rvs(p=cases_contacted, size=num_individuals)
params['n_age'] = np.sum([[i] * n_ages[i] for i in range(len(n_ages))])
params['I_COVID'] = scipy.stats.bernoulli.rvs(p=1 - fpr, size=num_individuals)
if not initial:
params['I_asymptoms'] = scipy.stats.bernoulli.rvs(p=frac_asymptoms, size=num_individuals) * params['I_COVID']
else:
params['I_asymptoms'] = np.zeros(num_individuals)
params['I_symptoms'] = 1 - params['I_asymptoms']
if not initial:
params['I_vacc'] = scipy.stats.bernoulli.rvs(p=frac_vacc, size=num_individuals)
else:
params['I_vacc'] = np.zeros(num_individuals) # TODO: HACK
params['t_incubation'] = np.maximum(scipy.stats.lognorm.rvs(s=0.65, scale=np.exp(1.57), size=num_individuals), 0) + t_exposure
params['t_incubation_infect'] = np.maximum(params['t_incubation'] - days_infectious_before_symptoms, 0)
if initial:
params['I_self_isolate'] = np.ones(num_individuals) * params['I_symptoms']
else:
params['I_self_isolate'] = scipy.stats.bernoulli.rvs(p=frac_self_isolate, size=num_individuals) * params['I_symptoms']
params['t_false_positive'] = np.random.geometric(p=symptom_false_positive_chance, size=num_individuals) + t_exposure
params['t_self_isolate'] = scipy.stats.gamma.rvs(loc=days_infectious_before_symptoms, scale=1.22, a=1/0.78, size=num_individuals) + params['t_incubation_infect']
if skip_days:
params['n_transmission_days'] = (
params['I_asymptoms'] * total_infectious_days +
params['I_symptoms'] * (1 - params['I_self_isolate']) * total_infectious_days +
params['I_symptoms'] * params['I_self_isolate'] * (params['t_self_isolate'] - params['t_incubation_infect']))
params['n_quarantine_days'] = np.zeros(num_individuals)
params['n_isolation_days'] = params['I_self_isolate'] * symp_quarantine_length
params['n_tests'] = np.zeros(num_individuals)
else:
(params['n_quarantine_days'], params['n_transmission_days'],
params['n_isolation_days'], params['n_tests'],
params['n_monitoring_days'], params['n_false_positive_isolation_days'],
params['n_billed_tracer_days']) = get_transmission_days(
t_exposure=params['t_exposure'], t_last_exposure=params['t_last_exposure'],
I_isolation=params['I_self_isolate'], t_isolation=params['t_self_isolate'],
t_infectious=params['t_incubation_infect'],
I_symptoms=params['I_symptoms'], t_symptoms=params['t_incubation'],
I_random_testing=random_testing, test_freq=test_freq,
test_delay=test_delay, t_false_positive=params['t_false_positive'])
params['id_original_case'] = (np.ones(num_individuals) * -1).astype(int)
params['id_infected_by'] = (np.ones(num_individuals) * -1).astype(int)
params['I_superspreader'] = scipy.stats.bernoulli.rvs(p=frac_SS, size=num_individuals) * params['I_COVID']
if initial:
params['I_test_positive'] = np.ones(num_individuals)
else:
params['I_test_positive'] = calc_test_results(num_individuals,
params['t_self_isolate'] - params['t_incubation_infect'],
params['I_COVID'], seed=seed)
params['I_infected_by_presymp'] = np.ones(num_individuals) * -1
params['I_infected_by_asymp'] = np.ones(num_individuals) * -1
return params
@njit
def draw_contacts(contact_days, n_transmission_days, t_incubation_infect, index, base_reduction=0., get_dummy=False, frac_vacc=0.,
seed=0):
np.random.seed(seed)
contact_list = List()
contacts = List()
selection = np.random.choice(len(contact_days))
for day in range(int(n_transmission_days)):
contacts.append(contact_days[selection])
for day in range(int(n_transmission_days)):
for (i, contact) in enumerate(contacts[day]):
# if (day == 0 or not contact[3] or np.random.binomial(n=1, p=(1. - 5./7.) ** day)) or get_dummy:
# if (day == 0 or not contact[3] or np.random.binomial(n=1, p=0.5)) or get_dummy:
if day == 0 or not contact[3] or get_dummy:
if not get_dummy:
if not contact[2] and not contact[3]:
if not np.random.binomial(n=1, p=1 - base_reduction):
continue
""" contact format: (age of contact, gender, is_household, is_daily,
t_exposure, index of infector, t_last_exposure, is_physical, contact_is_vacc) """
contact_ext = np.zeros(9)
contact_ext[0] = contact[0]
contact_ext[1] = contact[1]
contact_ext[2] = contact[2]
contact_ext[3] = contact[3]
if contact[3]:
contact_days_mask = np.random.binomial(n=1, p=1 - base_reduction, size=int(n_transmission_days))
if np.max(contact_days_mask) < 0.01:
continue
infection_days = np.where(contact_days_mask > 0.99)[0]
day_of_infection = infection_days[np.random.randint(len(infection_days))]
contact_ext[6] = t_incubation_infect + np.max(infection_days)
else:
day_of_infection = day
contact_ext[6] = day + t_incubation_infect
contact_ext[4] = day_of_infection + t_incubation_infect
contact_ext[5] = index
contact_ext[7] = contact[4]
contact_ext[8] = np.random.binomial(n=1, p=frac_vacc)
contact_list.append(contact_ext)
return contact_list
@njit
def draw_all_contacts(contacts_by_age, n_transmission_days, t_incubation_infect, num_individuals, base_reduction=0.0,
frac_vacc=0., seed=0):
np.random.seed(seed)
contact_dict = List()
for i in range(num_individuals):
a = draw_contacts(contact_days=contacts_by_age[0],
n_transmission_days=n_transmission_days[0],
t_incubation_infect=t_incubation_infect[0],
index=i, frac_vacc=frac_vacc, get_dummy=True)
if len(a) > 0:
break
for i in range(num_individuals):
contact_dict.append(a)
for i in range(num_individuals):
contact_dict[i] = draw_contacts(contact_days=contacts_by_age[i],
n_transmission_days=n_transmission_days[i],
t_incubation_infect=t_incubation_infect[i],
frac_vacc=frac_vacc,
index=i, base_reduction=base_reduction)
return contact_dict
""" draw index cases assuming uniform attack rate. draw contacts from POLYMOD """
def draw_contact_generation(index_cases, base_reduction=0.0, frac_vacc=0.0, seed=0):
num_individuals = len(index_cases['I_COVID'])
if num_individuals == 0:
return []
contacts_by_age = [age_to_contact_dict[index_cases['n_age'][i]] for i in range(num_individuals)]
contact_dict = draw_all_contacts(contacts_by_age=contacts_by_age,
n_transmission_days=index_cases['n_transmission_days'],
t_incubation_infect=index_cases['t_incubation_infect'],
num_individuals=num_individuals,
base_reduction=base_reduction,
frac_vacc=frac_vacc,
seed=seed)
return contact_dict
@jit(nopython=True, parallel=True)
def fill_QII(t_exposure, t_last_exposure, trace_delay, test_delay, t_self_isolate,
I_self_isolate, t_incubation_infect,
t_incubation, I_symptoms, I_household, t_false_positive,
id_infected_by, num_g1_cases, g0_I_self_isolate, g0_t_self_isolate,
g0_I_test_positive, g0_I_symptoms, g0_I_contacted,
g0_I_superspreader, false_negative_traces, trace=False, ttq=False,
quarantine_by_parent_case_release=False,
early_release_by_parent_case=np.zeros(1), early_release=False,
wait_before_testing=0, wait_until_testing=0,
ttq_double=False, dropouts=np.zeros(1).reshape(1, -1),
precalc_dropout=0,
monitor=False, seed=0, hold_hh=False,
quarantine_dropout_rate=quarantine_dropout_rate_default):
np.random.seed(seed)
n_quarantine_days = np.zeros(num_g1_cases)
n_transmission_days = np.zeros(num_g1_cases)
n_isolation_days = np.zeros(num_g1_cases)
n_monitoring_days = np.zeros(num_g1_cases)
n_billed_tracer_days = np.zeros(num_g1_cases)
n_tests = np.zeros(num_g1_cases)
n_false_positive_isolation_days = np.zeros(num_g1_cases)
secondary_cases_traced = 0
secondary_cases_monitored = 0
I_bidirectionally_traced = np.zeros(len(g0_I_self_isolate))
earliest_self_isolates = np.ones(len(g0_I_self_isolate)) * 10000
# if bidirectional_SS:
# for i in prange(num_g1_cases):
# parent_case = int(id_infected_by[i])
# if not g0_I_test_positive[parent_case] and g0_I_symptoms[parent_case] and I_self_isolate[i]:
# if not I_bidirectionally_traced[parent_case]:
# I_bidirectionally_traced[parent_case] = 1
# earliest_self_isolates[parent_case] = t_self_isolate[i]
# earliest_self_isolates[parent_case] = min(t_self_isolate[i], earliest_self_isolates[parent_case])
# for i in prange(len(g0_I_self_isolate)):
# if I_bidirectionally_traced[i] and not g0_I_self_isolate[i]:
# g0_I_test_positive[i] = calc_test_single(earliest_self_isolates[i], True)
# if ttq:
# for i in prange(num_g1_cases):
# parent_case = int(id_infected_by[i])
# if g0_I_test_positive[parent_case]:
# I_test_positive[i] = calc_test_single(g0_t_self_isolate[parent_case] + trace_delay - t_incubation[i], True)
for i in prange(num_g1_cases):
""" did the parent case test positive? """
parent_case = int(id_infected_by[i])
if (g0_I_test_positive[parent_case] and g0_I_self_isolate[parent_case] and g0_I_contacted[parent_case]
and trace and not false_negative_traces[i]):
secondary_cases_traced += 1
t_quarantine_start = g0_t_self_isolate[parent_case] + trace_delay + test_delay
I_test = False
t_test_day = -1
t_quarantine_end = max(t_last_exposure[i] + asymp_quarantine_length, t_quarantine_start)
if early_release and quarantine_by_parent_case_release[parent_case] and not ttq and not (hold_hh and I_household[i]):
t_quarantine_end = min(t_quarantine_end, t_quarantine_start + early_release_by_parent_case[parent_case])
if ttq:
if not early_release:
I_test = True
t_test_day = max(t_quarantine_start + wait_before_testing, t_last_exposure[i] + wait_until_testing + wait_before_testing)
elif early_release and quarantine_by_parent_case_release[parent_case] and not (hold_hh and I_household[i]):
I_test = True
t_test_day = t_quarantine_start + early_release_by_parent_case[parent_case]
else:
I_test = False
tmp = calculate_QII_days(t_exposure=t_exposure[i],
t_last_exposure=t_last_exposure[i],
t_quarantine=t_quarantine_start,
t_quarantine_end=t_quarantine_end,
I_quarantine=True,
t_isolation=t_self_isolate[i],
I_isolation=I_self_isolate[i],
t_false_positive=t_false_positive[i],
t_infectious=t_incubation_infect[i],
t_symptoms=t_incubation[i],
I_symptoms=I_symptoms[i],
I_monitor=monitor,
t_monitor=t_quarantine_start,
t_monitor_end=t_quarantine_end,
I_test=I_test,
t_test_day=t_test_day,
test_delay=test_delay,
I_double_test=ttq_double,
wait_before_testing=wait_before_testing,
I_early_release=early_release,
early_release_day=t_quarantine_start + early_release_by_parent_case[parent_case],
dropouts=dropouts[i].reshape(1, -1),
precalc_dropout=precalc_dropout,
quarantine_dropout_rate=quarantine_dropout_rate)
else:
tmp = calculate_QII_days(t_exposure=t_exposure[i],
t_last_exposure=t_last_exposure[i],
t_quarantine=0,
I_quarantine=False,
t_quarantine_end=0,
t_isolation=t_self_isolate[i],
I_isolation=I_self_isolate[i],
t_false_positive=t_false_positive[i],
t_infectious=t_incubation_infect[i],
t_symptoms=t_incubation[i],
I_symptoms=I_symptoms[i],
I_monitor=False,
t_monitor=-1,
I_test=False,
t_test_day=-1,
test_delay=test_delay,
quarantine_dropout_rate=quarantine_dropout_rate)
n_quarantine_days[i] += tmp[0]
n_isolation_days[i] += tmp[1]
n_transmission_days[i] += tmp[2]
n_tests[i] += tmp[3]
n_monitoring_days[i] += tmp[4]
n_false_positive_isolation_days[i] += tmp[5]
n_billed_tracer_days[i] += tmp[6]
return (n_quarantine_days, n_transmission_days, n_isolation_days, secondary_cases_traced, secondary_cases_monitored, n_monitoring_days,
n_tests, n_false_positive_isolation_days, n_billed_tracer_days)
# positives = [0]
# negatives = [0]
# days_rel_to_sympts = []
@njit
def calculate_QII_days(t_exposure, t_last_exposure,
t_infectious, t_false_positive,
I_quarantine=False, t_quarantine=0., t_quarantine_end=0.,
I_isolation=False, t_isolation=0.,
I_symptoms=False, t_symptoms=0.,
I_monitor=False, t_monitor=0., t_monitor_end=0.,
I_test=False, t_test_day=0., test_delay=0.,
I_random_testing=False, test_freq=0.,
I_double_test=False, wait_before_testing=0,
early_release_day=0, I_early_release=False,
dropouts=np.zeros(1).reshape(1, -1), precalc_dropout=0,
quarantine_dropout_rate=quarantine_dropout_rate_default):
# if I_random_testing and test_freq > 0.0:
# test_interval = 1 / test_freq
# else:
# test_interval = 10000
# first_test = t_infectious + np.random.uniform(0.0, test_interval)
quarantine_days = 0
isolation_days = 0
transmission_days = 0
monitoring_days = 0
false_isolation_days = 0
n_tests = 0
t_isolation_end = t_isolation + symp_quarantine_length
t_monitor_end = t_monitor + asymp_quarantine_length
if I_quarantine and I_isolation:
if t_quarantine_end > t_isolation_end:
t_quarantine_end = t_isolation_end
t_infectious_end = t_infectious + total_infectious_days
day = t_exposure
I_test_positive = False
symptoms_observed = False
tested = False
last_test_result = True
test_results_day_exists = False
test_results_day = 0
I_false_isolation = False
t_false_isolation = t_exposure
t_false_isolation_end = t_exposure
billed_tracer_days = 0
quarantine_dropout_chance = quarantine_dropout_rate
quarantine_dropout_rate_pos_test = 0.0 * quarantine_dropout_rate
quarantine_dropout_rate_not_released = quarantine_dropout_rate * (1 - dropout_reduction_for_symptoms)
quarantine_dropout_rate_neg_test = quarantine_dropout_rate * 2
# print("infected ", quarantine_dropout_chance)
while not (day >= t_quarantine_end and day >= t_isolation_end and day >= t_isolation_end and day >= t_infectious_end and day >= t_false_isolation_end):
# code.interact(local=locals())
# symptoms are developed during quarantine or already exist when quarantine started
if I_symptoms and day >= t_symptoms and ((I_quarantine and t_quarantine <= day < t_quarantine_end) or (I_monitor and t_monitor <= day < t_monitor_end)) and not symptoms_observed:
if I_isolation:
t_isolation = min(t_isolation, day + 1)
else:
t_isolation = day
I_isolation = True
t_isolation_end = t_isolation + symp_quarantine_length
t_quarantine_end = min(t_quarantine_end, t_isolation_end)
symptoms_observed = True
# is this a billed tracer day
if (I_quarantine and t_quarantine <= day < t_quarantine_end) or (I_monitor and t_monitor <= day < t_monitor_end):
billed_tracer_days += 1
# is this person quarantined today? do they drop out?
if t_infectious <= day < t_infectious_end:
if (not I_quarantine or not (t_quarantine <= day < t_quarantine_end)) and (not I_isolation or not (t_isolation <= day < t_isolation_end)):
transmission_days += 1
elif (I_isolation and t_isolation <= day < t_isolation_end) or (I_quarantine and t_quarantine <= day < t_quarantine_end) or (I_false_isolation and t_false_isolation <= day < t_false_isolation_end):
isolation_days += 1
elif I_quarantine and t_quarantine <= day < t_quarantine_end:
if (I_isolation and t_isolation <= day < t_isolation_end):
isolation_days += 1
elif I_false_isolation and t_false_isolation <= day < t_false_isolation_end:
false_isolation_days += 1
else:
quarantine_days += 1
elif I_isolation and t_isolation <= day < t_isolation_end:
isolation_days += 1
elif I_false_isolation and t_false_isolation <= day < t_false_isolation_end:
false_isolation_days += 1
elif I_monitor and t_monitor <= day < t_monitor_end:
monitoring_days += 1
if day >= t_false_positive and not I_false_isolation:
I_false_isolation = True
t_false_isolation = day
t_false_isolation_end = day + symp_quarantine_length
# adjust quarantine dropout chance
# print("day, dropout ", day, quarantine_dropout_chance)
if I_early_release and early_release_day <= day < early_release_day + 1:
quarantine_dropout_chance = quarantine_dropout_rate_not_released
if test_results_day_exists and test_results_day <= day < test_results_day + 1:
if I_test_positive:
quarantine_dropout_chance = quarantine_dropout_rate_pos_test
else:
quarantine_dropout_chance = quarantine_dropout_rate_neg_test
# drop out of quarantine
if I_quarantine and t_quarantine <= day < t_quarantine_end and not (
(I_isolation and t_isolation <= day < t_isolation_end) or
(I_false_isolation and t_false_isolation <= day <= t_false_isolation_end)):
if day - t_quarantine < precalc_dropout and quarantine_dropout_chance == quarantine_dropout_rate:
if dropouts[0][int(day - t_quarantine)] > 0.:
t_quarantine_end = day
t_monitor_end = day
elif np.random.binomial(n=1, p=quarantine_dropout_chance):
t_quarantine_end = day
t_monitor_end = day
# is this person tested today?
if I_double_test and day >= t_test_day and t_quarantine <= day < t_quarantine_end - test_delay:
I_test_positive = calc_test_single(day - t_symptoms, True)
test_results_day = day + test_delay
test_results_day_exists = True
tested = True
n_tests += 1
if not last_test_result and not I_test_positive:
t_quarantine_end = min(day + test_delay, t_quarantine_end)
I_double_test = False
else:
last_test_result = I_test_positive
t_test_day = day + test_delay + wait_before_testing
# print(I_test and day >= t_test_day and not tested and t_quarantine <= day < t_quarantine_end - test_delay)
if I_test and day >= t_test_day and not tested and t_quarantine <= day < t_quarantine_end - test_delay:
# if I_test and day >= t_test_day and not tested and (day - t_symptoms >= target_test_day):
I_test_positive = calc_test_single(day - t_symptoms, True)
test_results_day = day + test_delay
test_results_day_exists = True
# days_rel_to_sympts.append(day - t_symptoms)
if not I_test_positive:
# negatives[0] += 1
t_quarantine_end = min(day + test_delay, t_quarantine_end)
else:
pass
# positives[0] += 1
tested = True
n_tests += 1
# code.interact(local=locals())
if I_random_testing and not I_test_positive and np.random.binomial(n=1, p=test_freq):
# if I_random_testing and not I_test_positive and first_test <= day < t_infectious_end and 0 <= (day - first_test) % test_interval < 1:
I_test_positive = calc_test_single(day - t_symptoms, True)
if I_test_positive:
if I_isolation:
t_isolation = min(t_isolation, day)
else:
t_isolation = day
I_isolation = True
t_isolation_end = t_isolation + symp_quarantine_length
t_quarantine_end = min(t_quarantine_end, t_isolation_end)
tested = True
n_tests += 1
# infectious, but not quarantined or isolated
day += 1
# print("billed tracer days", billed_tracer_days, quarantine_days, isolation_days, t_isolation_end - t_isolation)
return (quarantine_days, isolation_days, transmission_days, n_tests, monitoring_days, false_isolation_days, billed_tracer_days)
@njit
def draw_infections_from_contacts(num_individuals, g0_COVID, g0_symptoms,
g0_original_case, g0_superspreader,
g0_test_positive, g0_incubation_infect,
g0_t_self_isolate, g0_I_contacted,
g0_contacts, trace_delay=0, test_delay=0,
trace=False, trace_superspreader=False,
ttq=False, early_release=False, vacc_eff=0.,
tfn=0.0, wait_before_testing=0, ttq_double=False,
household_SAR=base_household_cal, external_SAR=base_external_cal,
SS_mult=SS_mult_cal, phys_mult=phys_mult_cal,
monitor=False, seed=0):
np.random.seed(seed)
n_age = List()
t_exposure = List()
t_last_exposure = List()
original_case = List()
infected_by = List()
I_COVID = List()
I_household = List()
infected_by_presymp = List()
infected_by_asymp = List()
successful_traces = List()
quarantine_days_of_uninfected = 0
tests_of_uninfected = 0
monitoring_days_of_uninfected = 0
# SAR_SS_household = max(min(frac_infs_from_SS * symptoms_household_SAR / ((1 + frac_infs_from_SS) / (1 + 1 / freq_superspreaders)), 1), 0)
# SAR_SS_ext = max(min(frac_infs_from_SS * symptoms_external_SAR / ((1 + frac_infs_from_SS) / (1 + 1 / freq_superspreaders)), 1), 0)
# SAR_reg_household = max(min((symptoms_household_SAR - SAR_SS_household * freq_superspreaders) / (1 - freq_superspreaders), 1), 0)
# SAR_reg_ext = max(min((symptoms_external_SAR - SAR_SS_ext * freq_superspreaders) / (1 - freq_superspreaders), 1), 0)
uninfected_source = List()
uninfected_exposure = List()
uninfected_household = List()
household_exposures = 0
household_infections = 0
external_exposures = 0
external_infections = 0
num_downstream_contacts_by_id = np.zeros(num_individuals)
num_downstream_traces_by_id = np.zeros(num_individuals)
SARS = List()
for i in range(num_individuals):
if not g0_COVID[i]:
continue
for j in range(len(g0_contacts[i])):
num_downstream_contacts_by_id[i] += 1
traced = np.random.binomial(n=1, p=1 - tfn) > 0.99
if traced and g0_I_contacted[i] and trace:
num_downstream_traces_by_id[i] += 1
infected = False
SAR = 1.0
if g0_contacts[i][j][2]:
SAR = household_SAR
household_exposures += 1
else:
SAR = external_SAR
external_exposures += 1
if g0_contacts[i][j][7]:
SAR = SAR * phys_mult
if g0_superspreader[i]:
SAR = SAR * SS_mult
if not g0_symptoms[i]: # or g0_contacts[i][j][6] < days_infectious_before_symptoms:
SAR = SAR * asymp_infectiousness
if g0_symptoms[i] and g0_contacts[i][j][4] < g0_incubation_infect[i] + days_infectious_before_symptoms:
SAR = SAR * presymp_infectiousness
SAR = min(max(SAR, 0.), 1.)
if i == 0:
SARS.append(SAR)
infected = np.random.binomial(n=1, p=SAR)
if infected and g0_contacts[i][j][8]:
if np.random.binomial(n=1, p=vacc_eff):
infected = False
if infected:
if g0_contacts[i][j][2]:
household_infections += 1
else:
external_infections += 1
if g0_contacts[i][j][4] < g0_incubation_infect[i] + days_infectious_before_symptoms and g0_symptoms[i]:
infected_by_presymp.append(True)
else:
infected_by_presymp.append(False)
if not g0_symptoms[i]:
infected_by_asymp.append(True)
else:
infected_by_asymp.append(False)
successful_traces.append(traced)
n_age.append(g0_contacts[i][j][0])
t_exposure.append(g0_contacts[i][j][4])
t_last_exposure.append(g0_contacts[i][j][6])
infected_by.append(g0_contacts[i][j][5])
I_household.append(g0_contacts[i][j][2])
I_COVID.append(1)
if g0_original_case[i] == -1:
original_case.append(g0_contacts[i][j][5])
else:
original_case.append(g0_original_case[i])
else:
if g0_I_contacted[i] and trace and traced:
# trace_start = g0_t_self_isolate[i] + trace_delay + test_delay
# quarantine_end = asymp_quarantine_length + g0_contacts[i][j][6]
uninfected_source.append(i)
uninfected_exposure.append(g0_contacts[i][j][6])
uninfected_household.append(g0_contacts[i][j][2])
# print('SARS', SARS)
# print(g0_contacts[0])
return (n_age, t_exposure, t_last_exposure, original_case, infected_by, I_COVID, I_household, successful_traces,
quarantine_days_of_uninfected, tests_of_uninfected, monitoring_days_of_uninfected,
infected_by_presymp, infected_by_asymp, uninfected_source, uninfected_exposure, uninfected_household,
num_downstream_contacts_by_id, num_downstream_traces_by_id, household_infections / household_exposures if household_exposures > 0 else 0.,
external_infections / external_exposures if external_exposures > 0 else 0.)
@njit
def calc_pooled_tests(g1_infected_by, g1_t_incubation, g0_I_contacted,
g0_t_self_isolate, successful_traces, trace_delay, test_delay,
dropouts, test_day, seed=0):
positive_tests = np.zeros(len(g0_t_self_isolate))
dropout_sum = np.sum(dropouts, axis=1)
tests_per_parent_case = np.zeros(len(g0_t_self_isolate))
tests_per_case = np.zeros(len(g1_t_incubation))
for i in range(len(g1_I_symptoms)):
if not successful_traces[i] or dropout_sum[i] > 0. or not g0_I_contacted[parent_case]:
continue
parent_case = int(g1_infected_by[i])
if tests_per_parent_case[i] >= pooling_max:
continue
time_of_symptoms_rel_to_monitoring = g1_t_incubation[i] - (g0_t_self_isolate[parent_case] + trace_delay + test_delay)
positive_tests[parent_case] += calc_test_single(test_day - time_of_symptoms_rel_to_monitoring, True)
tests_per_parent_case[parent_case] += 1
if tests_per_parent_case[parent_case] < 2.:
tests_per_case[i] += 1
return (positive_tests, tests_per_case)
@njit
def calc_symptoms_by_day(g1_I_symptoms, g1_infected_by, g1_t_incubation, g1_false_positive, g0_I_contacted,
g0_t_self_isolate, successful_traces, trace_delay, test_delay,
uninf_g1_source, uninf_g1_false_positive,
dropouts, tracking_days):
symptoms_by_day = np.zeros((len(g0_t_self_isolate), tracking_days))
symptoms_by_day_infected = np.zeros((len(g0_t_self_isolate), tracking_days))
symptoms_by_day_uninf = np.zeros((len(g0_t_self_isolate), tracking_days))
time_of_symptoms_rel_to_monitoring = np.ones(len(g1_I_symptoms)) * -1
time_of_symptoms = np.ones(len(g1_I_symptoms)) * -1
# the infected
# print('initial', tracking_days, symptoms_by_day, dropouts)
for i in range(len(g1_I_symptoms)):
if not successful_traces[i]:
continue
seen_symptoms = False
dropped_out = False
parent_case = int(g1_infected_by[i])
if not g0_I_contacted[parent_case]:
continue
time_of_symptoms[i] = g1_false_positive[i]
if g1_I_symptoms[i]:
time_of_symptoms[i] = min(g1_false_positive[i], g1_t_incubation[i])
time_of_symptoms_rel_to_monitoring[i] = time_of_symptoms[i] - (g0_t_self_isolate[parent_case] + trace_delay + test_delay)
for j in range(tracking_days):
# print("sanity", i, parent_case, j)
if dropouts[i, j]:
dropped_out = True
if seen_symptoms:
symptoms_by_day[parent_case, j] += 1
symptoms_by_day_infected[parent_case, j] += 1
elif not dropped_out and j + g0_t_self_isolate[parent_case] + trace_delay + test_delay >= time_of_symptoms[i]:
seen_symptoms = True
symptoms_by_day[parent_case, j] += 1
symptoms_by_day_infected[parent_case, j] += 1
# print("stats", i, parent_case, g1_I_symptoms[i], time_of_symptoms[i], g1_false_positive[i], g1_t_incubation[i], 0 + g0_t_self_isolate[parent_case] + trace_delay + test_delay, symptoms_by_day)
# uninfected
for i in range(len(uninf_g1_source)):
seen_symptoms = False
dropped_out = False
parent_case = int(uninf_g1_source[i])
for j in range(tracking_days):
if dropouts[i + len(g1_I_symptoms), j]:
dropped_out = True
if seen_symptoms:
symptoms_by_day[parent_case, j] += 1
symptoms_by_day_uninf[parent_case, j] += 1
elif not dropped_out and j + g0_t_self_isolate[parent_case] + trace_delay + test_delay >= uninf_g1_false_positive[i]:
symptoms_by_day[parent_case, j] += 1
symptoms_by_day_uninf[parent_case, j] += 1
seen_symptoms = True
# print("trace starts", g0_t_self_isolate + trace_delay + test_delay)
# print("time of symptoms inf", time_of_symptoms_rel_to_monitoring)
# print("time of genuine symptoms", g1_t_incubation)
# print("infected symptoms by day", symptoms_by_day_infected)
# # print("inf dropouts", dropouts[:len(g1_t_incubation)])
# print("time of symptoms uninf", uninf_g1_false_positive)
# print("uninfected symptoms by day", symptoms_by_day_uninf)
# # print("uninf dropouts", dropouts[len(g1_t_incubation):])
return symptoms_by_day
@njit
def calc_quarantine_uninfected(trace_start, quarantine_end, t_exposure,
wait_before_testing=0, test_day=0,
test_delay=0,
early_release_day=0, early_release=False,
n_consec_test=0, test_release=False,
I_monitor=False, monitor_start=0,
monitor_end=0, symptom_false_positive=0,
dropouts=np.zeros(1).reshape(1, -1), precalc_dropout=0,
quarantine_dropout_rate=quarantine_dropout_rate_default):
day = trace_start
tests = 0
consec_negative_tests = 0
quarantine_dropout_chance = quarantine_dropout_rate
quarantine_dropout_rate_pos_test = 0.0 * quarantine_dropout_rate
quarantine_dropout_rate_not_released = quarantine_dropout_rate * (1 - dropout_reduction_for_symptoms)
quarantine_dropout_rate_neg_test = quarantine_dropout_rate * 2
quarantine_days = 0
monitor_days = 0
end = quarantine_end
test_results_day = 0
false_positive_isolation_days = 0
I_self_isolate = False
t_self_isolate = 0
t_self_isolate_end = -1
# print("np.shape", np.shape(dropouts), np.shape(dropouts)[0], day - trace_start, day - trace_start < precalc_dropout)
if I_monitor:
end = max(quarantine_end, monitor_end)
while day < quarantine_end or (I_monitor and day < monitor_end) or (I_self_isolate and day < t_self_isolate_end):
if trace_start <= day < quarantine_end:
quarantine_days += 1
elif I_self_isolate and t_self_isolate <= day < t_self_isolate_end:
false_positive_isolation_days += 1
elif I_monitor and monitor_start <= day < monitor_end:
monitor_days += 1
# print("monitoring sub", day, t_exposure, symptom_false_positive, not I_self_isolate)
if day >= t_exposure + symptom_false_positive and not I_self_isolate:
I_self_isolate = True
t_self_isolate = day
t_self_isolate_end = t_self_isolate + symp_quarantine_length
if test_release and day >= test_results_day and tests >= 1:
if consec_negative_tests >= n_consec_test:
quarantine_end = day
quarantine_dropout_chance = quarantine_dropout_rate_neg_test
if trace_start <= day < quarantine_end:
if precalc_dropout > 0 and day - trace_start < precalc_dropout:
if quarantine_dropout_chance == quarantine_dropout_rate and dropouts[0][int(math.floor(day - trace_start))] > 0.:
quarantine_end = day
if I_monitor:
monitor_end = day
elif np.random.binomial(n=1, p=quarantine_dropout_chance):
quarantine_end = day
if I_monitor:
monitor_end = day
if test_release and day - test_results_day >= wait_before_testing and day >= test_day and trace_start <= day < quarantine_end:
test_results_day = day + test_delay
tests += 1
consec_negative_tests += 1
if early_release and day >= early_release_day:
quarantine_dropout_chance = quarantine_dropout_rate_not_released
day += 1
# print(n_consec_test, test_release, initial_quarantine_length, quarantine_days)
# if report:
# print("monitoring", day, I_monitor, monitor_end, quarantine_end, trace_start, false_positive_isolation_days)
# print("quarantine", t_exposure, trace_start, quarantine_end)
return (quarantine_days, tests, monitor_days, false_positive_isolation_days)
@jit(nopython=True, parallel=True)
def calc_quarantine_days_of_uninfected(uninf_g1_source, uninf_g1_exposure_day, uninf_g1_household,
symptom_false_positive,
g1_I_symptoms, g1_t_symptoms,
g0_t_self_isolate, g0_test_positive,
g0_I_contacted, g0_superspreader, ttq=False,
ttq_double=False, trace=False,
trace_delay=0, test_delay=0, wait_before_testing=0,
wait_until_testing=0,
early_release=False,
quarantine_by_parent_case_release=np.zeros(1),
early_release_by_parent_case=np.zeros(1),
monitor=False, dropouts=np.zeros(1).reshape(1, -1),
precalc_dropout=0, quarantine_dropout_rate=quarantine_dropout_rate_default,
hold_hh=False,
seed=0):
np.random.seed(seed)
quarantine_days_of_uninfected = np.zeros(len(g0_t_self_isolate))
tests_of_uninfected = np.zeros(len(g0_t_self_isolate))
monitoring_days_of_uninfected = np.zeros(len(g0_t_self_isolate))
isolation_days_of_uninfected = np.zeros(len(g0_t_self_isolate))
total_contacts = 0.
released_contacts = 0.
isolation_events = 0.
# print("uninfected ", quarantine_dropout_rate)
for i in prange(len(uninf_g1_source)):
parent_case = int(uninf_g1_source[i])
if g0_I_contacted[parent_case] and trace:
total_contacts += 1
trace_start = g0_t_self_isolate[parent_case] + trace_delay + test_delay
quarantine_end = asymp_quarantine_length + uninf_g1_exposure_day[i]
test_day = max(uninf_g1_exposure_day[i] + wait_before_testing + wait_until_testing, trace_start + wait_before_testing)
monitor_start = trace_start
monitor_end = quarantine_end
if early_release:
if (not quarantine_by_parent_case_release[parent_case]) or (hold_hh and uninf_g1_household[i]):
tmp = calc_quarantine_uninfected(trace_start=trace_start,
quarantine_end=quarantine_end,
early_release=True,
early_release_day=early_release_by_parent_case[parent_case] + trace_start,
I_monitor=monitor,
monitor_start=monitor_start,
monitor_end=monitor_end,
symptom_false_positive=symptom_false_positive[i],
t_exposure=uninf_g1_exposure_day[i],
dropouts=dropouts[i].reshape(1, -1),
precalc_dropout=precalc_dropout,
quarantine_dropout_rate=quarantine_dropout_rate)
elif ttq:
tmp = calc_quarantine_uninfected(trace_start=trace_start,
quarantine_end=quarantine_end,
I_monitor=monitor,
n_consec_test=1, test_release=True,
test_delay=test_delay,
test_day=test_day,
wait_before_testing=wait_before_testing,
monitor_start=monitor_start,
monitor_end=monitor_end,
symptom_false_positive=symptom_false_positive[i],
t_exposure=uninf_g1_exposure_day[i],
dropouts=dropouts[i].reshape(1, -1),
precalc_dropout=precalc_dropout,
quarantine_dropout_rate=quarantine_dropout_rate)
released_contacts += 1
else:
tmp = calc_quarantine_uninfected(trace_start=trace_start,
quarantine_end=min(quarantine_end,
trace_start + early_release_by_parent_case[parent_case]),
I_monitor=monitor,
monitor_start=monitor_start,
monitor_end=monitor_end,
symptom_false_positive=symptom_false_positive[i],
t_exposure=uninf_g1_exposure_day[i],
dropouts=dropouts[i].reshape(1, -1),
precalc_dropout=precalc_dropout,
quarantine_dropout_rate=quarantine_dropout_rate)
released_contacts += 1
elif ttq:
tmp = calc_quarantine_uninfected(trace_start=trace_start, quarantine_end=quarantine_end,
wait_before_testing=wait_before_testing,
test_delay=test_delay,
test_day=test_day,
n_consec_test=1, test_release=True, I_monitor=monitor,
monitor_start=monitor_start,
monitor_end=monitor_end,
symptom_false_positive=symptom_false_positive[i],
t_exposure=uninf_g1_exposure_day[i],
quarantine_dropout_rate=quarantine_dropout_rate)
elif ttq_double:
tmp = calc_quarantine_uninfected(trace_start=trace_start, quarantine_end=quarantine_end,
wait_before_testing=wait_before_testing, test_delay=test_delay,
n_consec_test=2, test_release=True, I_monitor=monitor,
test_day=test_day,
monitor_start=monitor_start,
monitor_end=monitor_end,
symptom_false_positive=symptom_false_positive[i],
t_exposure=uninf_g1_exposure_day[i],
quarantine_dropout_rate=quarantine_dropout_rate)
else:
tmp = calc_quarantine_uninfected(trace_start=trace_start,
quarantine_end=quarantine_end, I_monitor=monitor,
monitor_start=monitor_start,
monitor_end=monitor_end,
symptom_false_positive=symptom_false_positive[i],
t_exposure=uninf_g1_exposure_day[i],
quarantine_dropout_rate=quarantine_dropout_rate)
quarantine_days_of_uninfected[parent_case] += tmp[0]
tests_of_uninfected[parent_case] += tmp[1]
monitoring_days_of_uninfected[parent_case] += tmp[2]
isolation_days_of_uninfected[parent_case] += tmp[3]
if tmp[3] > 0:
isolation_events += 1
if early_release:
release_pct = released_contacts / total_contacts
print("percent of contacts released", release_pct)
if monitor and total_contacts > 0 :
isolation_pct = isolation_events / total_contacts
print("isolation percentage of uninfected", isolation_pct)
# print("isolation days of uninfected", monitor, isolation_days_of_uninfected)
return (quarantine_days_of_uninfected, tests_of_uninfected, monitoring_days_of_uninfected, isolation_days_of_uninfected)
def draw_traced_generation_from_contacts(g0_cases, contacts, trace=False,
trace_superspreader=False, monitor_non_SS=False,
bidirectional_SS=False,
trace_delay=0, test_delay=0, trace_false_negative=0.0,
ttq=False, early_release=False,
early_release_day=0, early_release_threshold=0.0,
wait_before_testing=0, wait_until_testing=0, vacc_eff=0.,
ttq_double=False,
household_SAR=base_household_cal, external_SAR=base_external_cal,
SS_mult=SS_mult_cal, phys_mult=phys_mult_cal,
monitor=False, small_group_delay=False, small_group_threshold=0,
small_group_extra=0, cluster_pooling_release=False, seed=0,
quarantine_dropout_rate=quarantine_dropout_rate_default,
hold_hh=True):
# print("draw traced ", quarantine_dropout_rate)