def test_update(d):
d['smart_tracing_households_only'] = True
d['smart_tracing_actions'] = ['test', 'isolate']
d['test_reporting_lag'] = 0.5
# isolation
d['smart_tracing_policy_isolate'] = 'basic'
d['smart_tracing_isolated_contacts'] = 100000
# testing
d['smart_tracing_policy_test'] = 'basic'
d['smart_tracing_tested_contacts'] = 100000
d['trigger_tracing_after_posi_trace_test'] = False
return d
simulation_info = options_to_str(p_compliance=p_compliance)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=full_scale,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
print(f'{experiment_info} configuration done.')
def test_update(d):
d['smart_tracing_households_only'] = True
d['smart_tracing_actions'] = ['test', 'isolate']
d['test_reporting_lag'] = 0.5
# isolation
d['smart_tracing_policy_isolate'] = 'basic'
d['smart_tracing_isolated_contacts'] = 100000
# testing
d['smart_tracing_policy_test'] = 'basic'
d['smart_tracing_tested_contacts'] = 100000
d['trigger_tracing_after_posi_trace_test'] = False
return d
sim_info = options_to_str(
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
# baseline
experiment.add(
simulation_info=sim_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=full_scale,
store_mob=store_mob,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
d['smart_tracing_policy_isolate'] = 'advanced-threshold'
d['smart_tracing_isolation_threshold'] = smart_tracing_threshold
d['smart_tracing_isolated_contacts'] = 100000
d['smart_tracing_isolation_duration'] = 14 * TO_HOURS,
# testing
d['smart_tracing_policy_test'] = 'advanced-threshold'
d['smart_tracing_testing_threshold'] = smart_tracing_threshold
d['smart_tracing_tested_contacts'] = 100000
d['trigger_tracing_after_posi_trace_test'] = False
return d
simulation_info = options_to_str(
p_adoption=p_adoption,
test_lag=test_lag,
tracing_threshold=smart_tracing_threshold,
)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=full_scale,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
m = [
SocialDistancingForAllMeasure(
t_window=Interval(
measure_window_in_hours['start'],
measure_window_in_hours['end']),
p_stay_home=p_stay_home),
BetaMultiplierMeasureByType(
t_window=Interval(
measure_window_in_hours['start'],
measure_window_in_hours['end']),
beta_multiplier=calibration_lockdown_beta_multipliers)
]
simulation_info = options_to_str(iter=iteration)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
lockdown_measures_active=False,
test_update=None,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale)
print(f'{experiment_info} configuration done.')
measure_window_in_hours['end']),
beta_multiplier=beta_multiplier,
max_pos_tests_per_week_per_100k=max_pos_tests_per_week_per_100k,
intervention_times=intervention_times,
init_active=is_measure_active_initially),
UpperBoundCasesSocialDistancing(
t_window=Interval(measure_window_in_hours['start'],
measure_window_in_hours['end']),
p_stay_home=p_stay_home,
max_pos_tests_per_week_per_100k=max_pos_tests_per_week_per_100k,
intervention_times=intervention_times,
init_active=is_measure_active_initially)
]
simulation_info = options_to_str(
max_pos_tests_per_week_per_100k=50,
initially_active=is_measure_active_initially)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
lockdown_measures_active=False,
test_update=None,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
d['tests_per_batch'] = 100000
# isolation
d['smart_tracing_policy_isolate'] = 'basic'
d['smart_tracing_isolated_contacts'] = 100000
d['smart_tracing_isolation_duration'] = 14 * TO_HOURS,
# testing
d['smart_tracing_policy_test'] = test_policy
d['smart_tracing_tested_contacts'] = contacts_tested
return d
simulation_info = options_to_str(
tracing='+'.join(smart_tracing_actions),
delay=test_delay,
contacts_tested=contacts_tested,
test_policy=test_policy,
)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=full_scale,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
site_type: 1.0
for site_type in calibration_lockdown_site_closures
}
p_stay_home_dict = {
**p_stay_home_dict_closures,
**p_stay_home_dict_mobility_reduced
}
m = [
SocialDistancingBySiteTypeForAllMeasure(
t_window=Interval(measure_window_in_hours['start'],
measure_window_in_hours['end']),
p_stay_home_dict=p_stay_home_dict),
]
sim_info = options_to_str(validation_region=val_area)
experiment.add(simulation_info=sim_info,
country=val_country,
area=val_area,
measure_list=m,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale)
print(f'{experiment_info} configuration done.')
# execute all simulations
experiment.run_all()
d['smart_tracing_households_only'] = True
d['smart_tracing_actions'] = ['test', 'isolate']
d['test_reporting_lag'] = 0.5
# isolation
d['smart_tracing_policy_isolate'] = 'basic'
d['smart_tracing_isolated_contacts'] = 100000
# testing
d['smart_tracing_policy_test'] = 'basic'
d['smart_tracing_tested_contacts'] = 100000
d['trigger_tracing_after_posi_trace_test'] = False
return d
simulation_info = options_to_str(
max_incidence=max_incidence,
p_compliance=p_compliance,
)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
print(f'{experiment_info} configuration done.')
d['smart_tracing_households_only'] = True
d['smart_tracing_actions'] = ['test', 'isolate']
d['test_reporting_lag'] = 0.5
# isolation
d['smart_tracing_policy_isolate'] = 'basic'
d['smart_tracing_isolated_contacts'] = 100000
# testing
d['smart_tracing_policy_test'] = 'basic'
d['smart_tracing_tested_contacts'] = 100000
d['trigger_tracing_after_posi_trace_test'] = False
return d
simulation_info = options_to_str(
K_groups=groups,
p_compliance=p_compliance)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=full_scale,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
print(f'{experiment_info} configuration done.')
d['smart_tracing_policy_isolate'] = 'advanced-threshold'
d['smart_tracing_isolation_threshold'] = smart_tracing_threshold
d['smart_tracing_isolated_contacts'] = 100000
d['smart_tracing_isolation_duration'] = 14 * TO_HOURS,
# testing
d['smart_tracing_policy_test'] = 'advanced-threshold'
d['smart_tracing_testing_threshold'] = smart_tracing_threshold
d['smart_tracing_tested_contacts'] = 100000
d['trigger_tracing_after_posi_trace_test'] = False
return d
simulation_info = options_to_str(
p_tracing=p_adoption,
p_social_distancing=p_social_distancing,
test_lag=test_lag,
tracing_threshold=smart_tracing_threshold,
)
experiment.add(simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=full_scale,
expected_daily_base_expo_per100k=
expected_daily_base_expo_per100k)
m = [
SocialDistancingForAllMeasure(
t_window=Interval(
measure_window_in_hours['start'],
measure_window_in_hours['start'] + TO_HOURS * 7 * weeks),
p_stay_home=p_stay_home),
BetaMultiplierMeasureByType(
t_window=Interval(
measure_window_in_hours['start'],
measure_window_in_hours['start'] + TO_HOURS * 7 * weeks),
beta_multiplier=calibration_lockdown_beta_multipliers)
]
simulation_info = options_to_str(extended_lockdown_weeks=weeks)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
lockdown_measures_active=False,
test_update=None,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale)
print(f'{experiment_info} configuration done.')
# execute all simulations
end_date=end_date,
random_repeats=random_repeats,
full_scale=None,
verbose=verbose,
)
summary_paths = []
for exp, expparams in exps.items():
calibrated_params = get_calibrated_params(country=country,
area=area,
multi_beta_calibration=False,
maxiters=maxBOiters)
calibrated_params['beta_site'] = expparams[
'beta_scaling'] * calibrated_params['beta_site']
simulation_info = options_to_str(
exp=exp, beta_scaling=expparams['beta_scaling'])
summary_path = experiment_info + '/' + experiment_info + '-' + simulation_info
summary_paths.append(summary_path)
if not os.path.exists('summaries/' + summary_path + '.pk'):
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
test_update=None,
measure_list=[], # set automatically during lockdown
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=expparams['full_scale'])
full_scale=full_scale,
verbose=verbose,
)
# Isolate k groups for different numbers of groups
for groups in K_groups:
# measures
max_days = (pd.to_datetime(end_date) - pd.to_datetime(start_date)).days
m = [
SocialDistancingForKGroups(
t_window=Interval(measure_window_in_hours['start'], measure_window_in_hours['end']),
K=groups)
]
simulation_info = options_to_str(
K_groups=groups)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
lockdown_measures_active=False,
test_update=None,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale)
print(f'{experiment_info} configuration done.')
# execute all simulations
