def test_shorten_policies_start_only(policies):
start = pd.Timestamp("2020-10-01")
res = shorten_policies(policies, start_date=start)
expected = {
"drop_bc_too_late": {
"start": pd.Timestamp("2021-01-01"),
"end": pd.Timestamp("2021-01-30"),
"multiplier": 0.2,
},
"adjust_to_start_later": {
"start": start,
"end": pd.Timestamp("2020-11-01"),
"multiplier": 1.0,
},
"adjust_to_end_earlier": {
"start": pd.Timestamp("2020-11-02"),
"end": pd.Timestamp("2020-12-30"),
"multiplier": 1.0,
},
"unchanged": {
"start": pd.Timestamp("2020-11-02"),
"end": pd.Timestamp("2020-11-30"),
"multiplier": 1.0,
},
}
assert res == expected
def test_shorten_policies_end_only(policies):
end = pd.Timestamp("2020-12-01")
res = shorten_policies(policies, end_date=end)
expected = {
"drop_bc_too_early": {
"start": pd.Timestamp("2020-05-01"),
"end": pd.Timestamp("2020-05-30"),
"multiplier": 0.5,
},
"adjust_to_start_later": {
"start": pd.Timestamp("2020-09-01"),
"end": pd.Timestamp("2020-11-01"),
"multiplier": 1.0,
},
"adjust_to_end_earlier": {
"start": pd.Timestamp("2020-11-02"),
"end": end,
"multiplier": 1.0,
},
"unchanged": {
"start": pd.Timestamp("2020-11-02"),
"end": pd.Timestamp("2020-11-30"),
"multiplier": 1.0,
},
}
assert res == expected
def plus_10_pct_home_office_after_easter(paths, fixed_inputs):
"""Hypothetical scenario where there is 10 pct more home office after Easter."""
start_date = fixed_inputs["duration"]["start"]
end_date = fixed_inputs["duration"]["end"]
contact_models = fixed_inputs["contact_models"]
enacted_policies = get_enacted_policies(contact_models)
new_policies = _get_policies_with_multiplied_work_attend_multiplier_after_date(
enacted_policies=enacted_policies,
contact_models=contact_models,
multiplier=0.9,
split_date=AFTER_EASTER,
prefix="work_plus_10_pct_home_office_after_easter",
)
new_policies = shorten_policies(new_policies, start_date, end_date)
out = {
"contact_policies":
new_policies,
"vaccination_models":
_baseline_vaccination_models(paths, fixed_inputs),
"rapid_test_models":
_baseline_rapid_test_models(fixed_inputs),
"rapid_test_reaction_models":
_baseline_rapid_test_reaction_models(fixed_inputs),
}
return out
def strict_home_office_after_summer_scenario_start(paths, fixed_inputs):
"""Define strict home office after summer scenario starts.
The attendance multiplier is set to 0.54 which corresponds to the mean work
multiplier in April 2020.
"""
start_date = fixed_inputs["duration"]["start"]
end_date = fixed_inputs["duration"]["end"]
contact_models = fixed_inputs["contact_models"]
enacted_policies = get_enacted_policies(contact_models)
new_policies = _get_policies_with_different_work_attend_multiplier_after_date(
enacted_policies=enacted_policies,
contact_models=contact_models,
new_attend_multiplier=0.54,
split_date=SUMMER_SCENARIO_START,
prefix="work_strict_home_office_after_summer_scenario_start",
)
new_policies = shorten_policies(new_policies, start_date, end_date)
out = {
"contact_policies":
new_policies,
"vaccination_models":
_baseline_vaccination_models(paths, fixed_inputs),
"rapid_test_models":
_baseline_rapid_test_models(fixed_inputs),
"rapid_test_reaction_models":
_baseline_rapid_test_reaction_models(fixed_inputs),
}
return out
def close_educ_after_april_5(paths, fixed_inputs):
start_date = fixed_inputs["duration"]["start"]
end_date = fixed_inputs["duration"]["end"]
contact_models = fixed_inputs["contact_models"]
enacted_policies = get_enacted_policies(contact_models)
stays_same, to_change = split_policies(enacted_policies,
split_date=AFTER_EASTER)
keep = remove_educ_policies(to_change)
block_info = {
"prefix": "educ_closed_after_april_5",
"start_date": AFTER_EASTER,
"end_date": VERY_LATE,
}
new_educ_policies = shut_down_educ_models(contact_models=contact_models,
block_info=block_info,
educ_type="all")
new_policies = combine_dictionaries([stays_same, keep, new_educ_policies])
new_policies = shorten_policies(new_policies, start_date, end_date)
out = {
"vaccination_models":
_baseline_vaccination_models(paths, fixed_inputs),
"rapid_test_models":
_baseline_rapid_test_models(fixed_inputs),
"rapid_test_reaction_models":
_baseline_rapid_test_reaction_models(fixed_inputs),
"contact_policies":
new_policies,
}
return out
def only_strict_emergency_care_after_april_5(paths, fixed_inputs):
start_date = fixed_inputs["duration"]["start"]
end_date = fixed_inputs["duration"]["end"]
contact_models = fixed_inputs["contact_models"]
enacted_policies = get_enacted_policies(contact_models)
stays_same, to_change = split_policies(enacted_policies,
split_date=AFTER_EASTER)
keep = remove_educ_policies(to_change)
block_info = {
"prefix": "emergency_care_after_april_5",
"start_date": AFTER_EASTER,
"end_date": VERY_LATE,
}
new_young_educ_policies = apply_emergency_care_policies(
contact_models=contact_models,
block_info=block_info,
educ_type="young_educ",
attend_multiplier=0.25,
hygiene_multiplier=HYGIENE_MULTIPLIER,
)
school_options = get_school_options_for_strict_emergency_care()
new_school_policies = apply_mixed_educ_policies(
contact_models=contact_models,
block_info=block_info,
educ_type="school",
**school_options,
)
new_policies = combine_dictionaries(
[stays_same, keep, new_young_educ_policies, new_school_policies])
new_policies = shorten_policies(new_policies, start_date, end_date)
out = {
"vaccination_models":
_baseline_vaccination_models(paths, fixed_inputs),
"rapid_test_models":
_baseline_rapid_test_models(fixed_inputs),
"rapid_test_reaction_models":
_baseline_rapid_test_reaction_models(fixed_inputs),
"contact_policies":
new_policies,
}
return out
def robustness_check(paths, fixed_inputs):
"""Change the inputs to remove knowledge of the time after March 1st.
This function does the following:
- fix the work attend multiplier to the value of end of February
- use a simple linear extrapolation of the February vaccination data.
"""
exercise_start = pd.Timestamp("2021-03-01")
start_date = fixed_inputs["duration"]["start"]
end_date = fixed_inputs["duration"]["end"]
init_start = start_date - pd.Timedelta(31, unit="D")
contact_models = fixed_inputs["contact_models"]
enacted_policies = get_enacted_policies(contact_models)
# set work multiplier instead of using the
policies_with_fixed_work_multiplier = (
_get_policies_with_different_work_attend_multiplier_after_date(
enacted_policies=enacted_policies,
contact_models=contact_models,
new_attend_multiplier=0.75,
split_date=exercise_start,
prefix="work_fixed_work_multiplier_after_easter",
))
policies_with_fixed_work_multiplier = shorten_policies(
policies_with_fixed_work_multiplier, start_date, end_date)
# replace vaccinations with prediction from February
vaccination_shares = pd.read_pickle(paths["vaccination_shares"])
vaccination_models = _replace_vaccination_shares_with_prediction_from_feb(
vaccination_shares, init_start)
out = {
"contact_policies":
policies_with_fixed_work_multiplier,
"vaccination_models":
vaccination_models,
"rapid_test_models":
_baseline_rapid_test_models(fixed_inputs),
"rapid_test_reaction_models":
_baseline_rapid_test_reaction_models(fixed_inputs),
}
return out
def _open_all_educ_after_date(paths,
fixed_inputs,
split_date,
multiplier=HYGIENE_MULTIPLIER):
start_date = fixed_inputs["duration"]["start"]
end_date = fixed_inputs["duration"]["end"]
contact_models = fixed_inputs["contact_models"]
enacted_policies = get_enacted_policies(contact_models)
stays_same, to_change = split_policies(enacted_policies,
split_date=split_date)
after_split_without_educ_policies = remove_educ_policies(to_change)
block_info = {
"prefix": f"open_all_educ_after_{pd.Timestamp(split_date).date()}",
"start_date": split_date,
"end_date": VERY_LATE,
}
new_educ_policies = reduce_educ_models(
contact_models=contact_models,
block_info=block_info,
educ_type="all",
multiplier=multiplier,
)
new_policies = combine_dictionaries(
[stays_same, after_split_without_educ_policies, new_educ_policies])
new_policies = shorten_policies(new_policies, start_date, end_date)
out = {
"contact_policies":
new_policies,
"vaccination_models":
_baseline_vaccination_models(paths, fixed_inputs),
"rapid_test_models":
_baseline_rapid_test_models(fixed_inputs),
"rapid_test_reaction_models":
_baseline_rapid_test_reaction_models(fixed_inputs),
}
return out
def test_shorten_policies_both_dates(policies):
start = pd.Timestamp("2020-10-01")
end = pd.Timestamp("2020-12-01")
res = shorten_policies(policies, start_date=start, end_date=end)
expected = {
"adjust_to_start_later": {
"start": start,
"end": pd.Timestamp("2020-11-01"),
"multiplier": 1.0,
},
"adjust_to_end_earlier": {
"start": pd.Timestamp("2020-11-02"),
"end": end,
"multiplier": 1.0,
},
"unchanged": {
"start": pd.Timestamp("2020-11-02"),
"end": pd.Timestamp("2020-11-30"),
"multiplier": 1.0,
},
}
assert res == expected
def _baseline_policies(fixed_inputs):
start_date = fixed_inputs["duration"]["start"]
end_date = fixed_inputs["duration"]["end"]
policies = get_enacted_policies(fixed_inputs["contact_models"])
policies = shorten_policies(policies, start_date, end_date)
return policies