def task_plot_combined_rapid_test_demand_params(depends_on, func, produces):
params = pd.read_pickle(depends_on["params"])
params = func(params)
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="indexing past lexsort depth may impact performance.")
educ_workers_params = params.loc[("rapid_test_demand",
"educ_worker_shares")]
students_params = params.loc[("rapid_test_demand", "student_shares")]
work_offer_params = params.loc[("rapid_test_demand",
"share_workers_receiving_offer")]
private_demand_params = params.loc[("rapid_test_demand",
"private_demand")]
work_accept_params = params.loc[("rapid_test_demand",
"share_accepting_work_offer")]
# educ demand
share_educ_workers = get_piecewise_linear_interpolation(
educ_workers_params)
share_educ_workers = share_educ_workers.loc[PLOT_START_DATE:PLOT_END_DATE]
share_students = get_piecewise_linear_interpolation(students_params)
share_students = share_students.loc[PLOT_START_DATE:PLOT_END_DATE]
# worker demand
share_workers_receiving_offer = get_piecewise_linear_interpolation(
work_offer_params)
share_workers_receiving_offer = share_workers_receiving_offer.loc[
PLOT_START_DATE:PLOT_END_DATE]
share_workers_accepting_offer = get_piecewise_linear_interpolation(
work_accept_params)
share_workers = share_workers_receiving_offer * share_workers_accepting_offer
# private demand
private_demand_shares = get_piecewise_linear_interpolation(
private_demand_params)
private_demand_shares = private_demand_shares.loc[
PLOT_START_DATE:PLOT_END_DATE]
fig = _plot_rapid_test_demand_shares(
share_educ_workers=share_educ_workers,
share_students=share_students,
share_workers=share_workers,
private_demand_shares=private_demand_shares,
)
fig.savefig(produces["figure"])
plt.close()
if "data" in produces.keys():
df = pd.DataFrame({
"share_educ_workers": share_educ_workers,
"share_students": share_students,
"share_workers": share_workers,
"private_demand_shares": private_demand_shares,
})
df.round(3).to_csv(produces["data"])
def _check_no_work_rapid_tests(new_params):
accept_params = new_params.loc[
("rapid_test_demand", "share_accepting_work_offer"), "value"
]
accept_time_series = get_piecewise_linear_interpolation(params_slice=accept_params)
assert (accept_time_series == 0).all()
offer_params = new_params.loc[
("rapid_test_demand", "share_workers_receiving_offer"), "value"
]
offer_time_series = get_piecewise_linear_interpolation(params_slice=offer_params)
assert (offer_time_series == 0).all()
def _check_school_demand_is_zero(new_params):
teacher_params = new_params.loc[
("rapid_test_demand", "educ_worker_shares"), "value"
]
teacher_time_series = get_piecewise_linear_interpolation(
params_slice=teacher_params
)
assert (teacher_time_series == 0).all()
student_params = new_params.loc[("rapid_test_demand", "student_shares"), "value"]
student_time_series = get_piecewise_linear_interpolation(
params_slice=student_params
)
assert (student_time_series == 0).all()
def test_mandatory_work_rapid_tests_after_easter(params):
new_params = params_scenarios.mandatory_work_rapid_tests_after_easter(params)
accept_params = new_params.loc[
("rapid_test_demand", "share_accepting_work_offer"), "value"
]
accept_time_series = get_piecewise_linear_interpolation(params_slice=accept_params)
assert (accept_time_series[:"2021-04-05"] < 0.95).all()
assert (accept_time_series["2021-04-06":] == 0.95).all()
offer_params = new_params.loc[
("rapid_test_demand", "share_workers_receiving_offer"), "value"
]
offer_time_series = get_piecewise_linear_interpolation(params_slice=offer_params)
assert (offer_time_series[:"2021-04-05"] < 0.95).all()
assert (offer_time_series["2021-04-06":] == 0.95).all()
def task_plot_share_of_workers_receiving_test_offer(depends_on, produces):
params = pd.read_pickle(depends_on["params"])
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="indexing past lexsort depth may impact performance."
)
params_slice = params.loc[
("rapid_test_demand", "share_workers_receiving_offer")
]
share_workers_receiving_offer = get_piecewise_linear_interpolation(params_slice)
share_workers_receiving_offer = share_workers_receiving_offer.loc[
PLOT_START_DATE:PLOT_END_DATE
]
fig, ax = plt.subplots(figsize=PLOT_SIZE)
sns.lineplot(
x=share_workers_receiving_offer.index,
y=share_workers_receiving_offer,
ax=ax,
)
ax.set_title("Share Workers With Work Contacts With Rapid Test Offer At Work")
fig, ax = style_plot(fig, ax)
fig.tight_layout()
fig.savefig(produces)
plt.close()
def task_plot_private_test_demand_shares(depends_on, produces):
params = pd.read_pickle(depends_on["params"])
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="indexing past lexsort depth may impact performance.")
params_slice = params.loc[("rapid_test_demand", "private_demand")]
private_demand_shares = get_piecewise_linear_interpolation(params_slice)
private_demand_shares = private_demand_shares.loc[
PLOT_START_DATE:PLOT_END_DATE]
fig, ax = plt.subplots(figsize=PLOT_SIZE)
sns.lineplot(
x=private_demand_shares.index,
y=private_demand_shares,
ax=ax,
)
ax.set_title(
"Private Rapid Test Demand\n"
"(Share of Individuals who Do a Rapid Test \n"
"When a Household Member Tests Positive Or Becomes Symptomatic Or \n"
"When Developing Symptoms but not Receiving a Rapid Test Or \n"
"When Participating in Some Private Events)")
fig, ax = style_plot(fig, ax)
fig.tight_layout()
fig.savefig(produces)
plt.close()
def test_no_rapid_tests_at_schools_and_private(params):
new_params = params_scenarios.no_rapid_tests_at_schools_and_private(params)
_check_school_demand_is_zero(new_params)
private_slice = new_params.loc[("rapid_test_demand", "private_demand"), "value"]
private_series = get_piecewise_linear_interpolation(params_slice=private_slice)
assert (private_series == 0).all()
def test_keep_work_offer_share_at_23_pct_after_easter(params):
new_params = params_scenarios.keep_work_offer_share_at_23_pct_after_easter(params)
offer_params = new_params.loc[
("rapid_test_demand", "share_workers_receiving_offer"), "value"
]
time_series = get_piecewise_linear_interpolation(params_slice=offer_params)
assert time_series["2021-04-06"] == 0.23
assert (time_series["2021-04-06":] == 0.23).all()
def task_plot_overall_share_known_cases(depends_on, produces):
df_old = pd.read_csv(depends_on["old"])
old_share_known = _calculate_share_known_cases(
df_old)[PLOT_START_DATE:"2020-12-24"]
df_new = pd.read_csv(depends_on["new"])
new_share_known = _calculate_share_known_cases(df_new)["2020-12-25":]
share_known = pd.concat([old_share_known, new_share_known])
share_known.index = share_known.index.normalize()
assert not share_known.index.duplicated().any()
dates = share_known.index
expected_dates = pd.date_range(dates.min(), dates.max())
missing_dates = [str(x.date()) for x in expected_dates if x not in dates]
assert (len(missing_dates) == 0
), f"There are missing dates in the share_known: {missing_dates}"
share_known = share_known.loc[PLOT_START_DATE:"2020-12-28"]
params = pd.read_pickle(depends_on["params"])
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="indexing past lexsort depth may impact performance.")
params_slice = params.loc[("share_known_cases", "share_known_cases")]
share_known_from_params = get_piecewise_linear_interpolation(params_slice)
share_known_from_params = share_known_from_params.loc[
PLOT_START_DATE:PLOT_END_DATE]
fig, ax = plt.subplots(figsize=PLOT_SIZE)
sns.lineplot(
x=share_known.index,
y=share_known,
ax=ax,
label="Dunkelzifferradar",
alpha=0.6,
linewidth=3.0,
color=BLUE,
)
sns.lineplot(
x=share_known_from_params.index,
y=share_known_from_params,
ax=ax,
label="Interpolated and Extrapolated",
alpha=0.6,
linewidth=3.0,
color=RED,
)
fig, ax = style_plot(fig, ax)
ax.set_ylabel("share of cases that are detected")
fig.tight_layout()
ax.axvline(pd.Timestamp("2020-12-24"), alpha=0.6, color="k")
fig.savefig(produces["share_known_cases_fig"])
plt.close()
def task_plot_share_of_educ_participants_with_rapid_test(depends_on, produces):
params = pd.read_pickle(depends_on["params"])
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="indexing past lexsort depth may impact performance.")
educ_workers_params = params.loc[("rapid_test_demand",
"educ_worker_shares")]
students_params = params.loc[("rapid_test_demand", "student_shares")]
share_educ_workers = get_piecewise_linear_interpolation(
educ_workers_params)
share_educ_workers = share_educ_workers.loc[PLOT_START_DATE:PLOT_END_DATE]
share_students = get_piecewise_linear_interpolation(students_params)
share_students = share_students.loc[PLOT_START_DATE:PLOT_END_DATE]
fig, ax = plt.subplots(figsize=PLOT_SIZE)
sns.lineplot(
x=share_educ_workers.index,
y=share_educ_workers,
ax=ax,
label="Teachers (School, Preschool, Nursery)",
)
sns.lineplot(
x=share_students.index,
y=share_students,
ax=ax,
label="School Students",
)
ax.set_title("Share of Students and Teachers Receiving Rapid Tests")
fig, ax = style_plot(fig, ax)
fig.tight_layout()
fig.savefig(produces)
plt.close()
def test_start_all_rapid_tests_after_easter(params):
new_params = params_scenarios.start_all_rapid_tests_after_easter(params)
slice_tuples = [
("rapid_test_demand", "private_demand"),
("rapid_test_demand", "educ_worker_shares"),
("rapid_test_demand", "student_shares"),
("rapid_test_demand", "share_accepting_work_offer"),
("rapid_test_demand", "share_workers_receiving_offer"),
]
for loc in slice_tuples:
params_slice = new_params.loc[loc, "value"]
time_series = get_piecewise_linear_interpolation(params_slice=params_slice)
assert (time_series[:"2021-04-05"] == 0).all()
assert (time_series["2021-04-06":] > 0).all()
def _build_new_date_params(before_params_slice, change_date, new_val):
before = get_piecewise_linear_interpolation(before_params_slice)
day_before = change_date - pd.Timedelta(days=1)
if day_before in before:
val_before_change = before.loc[day_before]
else:
val_before_change = before.iloc[-1]
new_params_slice = before_params_slice.copy(deep=True)
new_params_slice.index = pd.DatetimeIndex(new_params_slice.index)
# remove old change points after change date
new_params_slice = new_params_slice[:day_before]
# implement the switch on the change date
new_params_slice.loc[day_before] = val_before_change
new_params_slice.loc[change_date] = new_val
# maintain the new value over time
new_params_slice.loc[pd.Timestamp("2025-12-31")] = new_val
new_params_slice = new_params_slice.sort_index()
return new_params_slice
def test_no_private_test_demand(params):
new_params = params_scenarios.no_private_rapid_test_demand(params)
comparison = create_comparison_df(new_params=new_params, old_params=params)
loc = ("rapid_test_demand", "private_demand")
private_demand_params = params.loc[loc, "value"]
should_have_changed = private_demand_params[private_demand_params != 0]
expected = pd.DataFrame()
expected["before"] = should_have_changed
expected["name"] = should_have_changed.index
expected["after"] = 0.0
expected["category"] = "rapid_test_demand"
expected["subcategory"] = "private_demand"
expected = expected.set_index(["category", "subcategory", "name"])
assert comparison.equals(expected)
private_series = get_piecewise_linear_interpolation(
params_slice=new_params.loc[loc, "value"]
)
assert (private_series == 0).all()
def load_simulation_inputs(
scenario,
start_date,
end_date,
debug,
group_share_known_case_path=None,
period_outputs=False,
return_last_states=False,
initial_states_path=None,
is_resumed=False,
rapid_test_statistics_path=None,
):
"""Load the simulation inputs.
Does **not** include: params, path, seed.
Args:
scenario (str): string specifying the scenario. A function with the
same name must exist in src.simulation.scenario_simulation_inputs.
start_date (str): date on which the simulation starts. Data must be available
for at least a month before the start date for the burn in period.
end_date (str): date on which the simulation ends.
debug (bool): Whether to use the debug or the full initial states.
group_share_known_case_path (pathlib.Path, str or None): if not None, the group
share known cases are loaded from this path and used for the creation of the
initial conditions.
period_outputs (bool, optional): whether to use period_outputs instead of saving
the time series. Default is False.
return_last_states (bool, optional): if True, the last states are returned as
part of the simulation result.
initial_states_path (pathlib.Path, optional): Path to the initial states.
If not given the standard initial states are used.
is_resumed (bool, optional): if True, the initial_states_path must be given. In
that case no initial conditions are created
rapid_test_statistics_path (Path, optional): where to save rapid test
statistics.
Returns:
dict: Dictionary with most arguments of get_simulate_func. Keys are:
- initial_states
- contact_models
- duration
- events
- saved_columns
- virus_strains
- derived_state_variables
- seasonality_factor_model
- initial_conditions
- susceptibility_factor_model
- testing_demand_models
- testing_allocation_models
- testing_processing_models
- period_outputs
- return_last_states
- return_time_series
- contact_policies
- vaccination_models
- rapid_test_models
- rapid_test_reaction_models
"""
if is_resumed:
assert (
initial_states_path is not None
), "You must specify the path to the initial states if you resume a simulation."
start_date = pd.Timestamp(start_date)
end_date = pd.Timestamp(end_date)
paths = get_simulation_dependencies(
debug=debug,
is_resumed=is_resumed,
)
if rapid_test_statistics_path is not None:
paths["rapid_test_statistics_path"] = rapid_test_statistics_path
if initial_states_path is None:
initial_states_path = paths["initial_states"]
if initial_states_path.suffix == ".pkl":
initial_states = pd.read_pickle(initial_states_path)
elif initial_states_path.suffix == ".parquet":
initial_states = pd.read_parquet(paths["initial_states"])
contact_models = get_all_contact_models()
# process dates
one_day = pd.Timedelta(1, unit="D")
init_start = start_date - pd.Timedelta(31, unit="D")
init_end = start_date - one_day
duration = {"start": start_date, "end": end_date}
# testing models
share_of_tests_for_symptomatics_series = pd.read_pickle(
paths["share_of_tests_for_symptomatics_series"]
)
test_start = init_start - one_day
test_end = end_date + one_day
test_demand_func = partial(
demand_test,
share_of_tests_for_symptomatics_series=share_of_tests_for_symptomatics_series,
)
testing_demand_models = {
"symptoms": {
"model": test_demand_func,
"start": test_start,
"end": test_end,
}
}
testing_allocation_models = {
"direct_allocation": {
"model": allocate_tests,
"start": test_start,
"end": test_end,
}
}
testing_processing_models = {
"direct_processing": {
"model": process_tests,
"start": test_start,
"end": test_end,
}
}
saved_columns = {
"initial_states": ["age_group_rki"],
"disease_states": ["newly_infected", "newly_deceased", "ever_infected"],
"time": ["date"],
"other": [
"new_known_case",
"virus_strain",
"n_has_infected",
"channel_infected_by_contact",
"state",
"knows_currently_infected",
"currently_infected",
],
}
if not is_resumed:
virus_shares = pd.read_pickle(paths["virus_shares"])
rki_infections = pd.read_pickle(paths["rki"])
group_weights = pd.read_pickle(paths["rki_age_groups"])["weight"]
if group_share_known_case_path is not None:
group_share_known_cases = pd.read_pickle(group_share_known_case_path)
else:
group_share_known_cases = None
params = pd.read_pickle(paths["params"])
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="indexing past lexsort depth may impact performance."
)
params_slice = params.loc[("share_known_cases", "share_known_cases")]
overall_share_known_cases = get_piecewise_linear_interpolation(params_slice)
initial_conditions = create_initial_conditions(
start=init_start,
end=init_end,
seed=3930,
reporting_delay=5,
synthetic_data=initial_states[["county", "age_group_rki"]],
empirical_infections=rki_infections,
virus_shares=virus_shares,
overall_share_known_cases=overall_share_known_cases,
group_share_known_cases=group_share_known_cases,
group_weights=group_weights,
)
else:
initial_conditions = None
seasonality_factor_model = partial(seasonality_model, contact_models=contact_models)
def _currently_infected(df):
return df["infectious"] | df["symptomatic"] | (df["cd_infectious_true"] >= 0)
def _knows_currently_infected(df):
return df["knows_immune"] & df["currently_infected"]
derived_state_variables = {
"currently_infected": _currently_infected,
"knows_currently_infected": _knows_currently_infected,
}
events = {
"introduce_b117": {"model": introduce_b117},
"introduce_delta": {"model": introduce_delta},
}
fixed_inputs = {
"initial_states": initial_states,
"contact_models": contact_models,
"duration": duration,
"events": events,
"testing_demand_models": testing_demand_models,
"testing_allocation_models": testing_allocation_models,
"testing_processing_models": testing_processing_models,
"saved_columns": saved_columns,
"initial_conditions": initial_conditions,
"susceptibility_factor_model": calculate_susceptibility,
"virus_strains": ["base_strain", "b117", "delta"],
"seasonality_factor_model": seasonality_factor_model,
"derived_state_variables": derived_state_variables,
"return_last_states": return_last_states,
}
if period_outputs:
fixed_inputs["period_outputs"] = create_period_outputs()
fixed_inputs["return_time_series"] = False
scenario_func = getattr(scenario_simulation_inputs, scenario)
scenario_inputs = scenario_func(paths, fixed_inputs)
simulation_inputs = combine_dictionaries([fixed_inputs, scenario_inputs])
return simulation_inputs
def _get_school_multipliers(start_date):
share_age_for_emergency_care = 0.5
share_in_graduating_classes = 0.25 # 16, 17 and 18 year olds
share_in_primary = 0.3
a_b_multiplier = 0.5
share_getting_strict_emergency_care = 0.2
share_getting_generous_emergency_care = 0.3
strict_emergency_care_multiplier = (
share_age_for_emergency_care
* share_getting_strict_emergency_care
* HYGIENE_MULTIPLIER
)
generous_emergency_care_multiplier = (
share_in_graduating_classes * a_b_multiplier
+ share_age_for_emergency_care * share_getting_generous_emergency_care
) * HYGIENE_MULTIPLIER
feb_to_march_a_b_share = (
share_in_primary + share_in_graduating_classes
) * a_b_multiplier
feb_to_march_emergency_share = (
share_age_for_emergency_care
* share_getting_generous_emergency_care
* a_b_multiplier
)
feb_to_march_multiplier = (
feb_to_march_a_b_share + feb_to_march_emergency_share
) * HYGIENE_MULTIPLIER
a_b_for_most_multiplier = (
a_b_multiplier + feb_to_march_emergency_share * a_b_multiplier
)
school_multiplier_without_vacations = pd.Series(
{
start_date: 1.0,
"2020-11-01": 1.0,
"2020-11-02": HYGIENE_MULTIPLIER,
"2020-12-15": HYGIENE_MULTIPLIER,
# strict emergency care
"2020-12-16": strict_emergency_care_multiplier,
"2021-01-10": strict_emergency_care_multiplier,
# generous emergency care
"2021-01-11": generous_emergency_care_multiplier,
"2021-02-21": generous_emergency_care_multiplier,
# primary and graduating in A / B
"2021-02-22": feb_to_march_multiplier,
"2021-03-14": feb_to_march_multiplier,
# mid March until Easter: A / B for most
"2021-03-15": a_b_for_most_multiplier,
"2021-04-05": a_b_for_most_multiplier,
# easter until may:
"2021-04-06": generous_emergency_care_multiplier,
"2021-04-30": generous_emergency_care_multiplier,
# may
"2021-05-01": a_b_for_most_multiplier,
"2021-05-31": a_b_for_most_multiplier,
}
)
school_multiplier_without_vacations = get_piecewise_linear_interpolation(
school_multiplier_without_vacations
)
school_multiplier_with_vacations = pd.Series(
{
start_date: 1.0,
# fall vacation:
# first start on 2020-10-05. last end 2020-11-06
# on average from 2020-10-13 to 2020-10-23
# there was no overlap in the fall vacation dates between states.
"2020-10-05": 1.0,
"2020-10-13": 0.3, # number to cover that many states had fall vacation
"2020-10-23": 0.3,
"2020-11-06": HYGIENE_MULTIPLIER,
# strict emergency care started in the week before Christmas
"2020-12-15": HYGIENE_MULTIPLIER,
"2020-12-16": strict_emergency_care_multiplier,
# Christmas vacations started on 2002-12-21 in most states.
# Christmas vacations ended between 2021-01-02 and 2021-01-10.
"2020-12-20": strict_emergency_care_multiplier,
"2020-12-21": 0.0,
"2021-01-02": 0.0,
"2021-01-10": strict_emergency_care_multiplier,
# generous emergency care
# winter vacations were from 2021-01-25 until 2021-03-12 depending on the
# state and only short so we ignore them here.
"2021-01-11": generous_emergency_care_multiplier,
"2021-02-21": generous_emergency_care_multiplier,
# primary and graduating in A / B
"2021-02-22": feb_to_march_multiplier,
"2021-03-14": feb_to_march_multiplier,
# mid March until Easter: A / B for most
"2021-03-15": a_b_for_most_multiplier,
"2021-03-26": a_b_for_most_multiplier,
# Easter vacations started on 2021-03-29 in most states
# and ended between 2021-04-05 and 2021-04-16, for most on 2021-04-10
"2021-03-29": 0.0,
"2021-04-06": 0.0,
"2021-04-10": 0.1, # some states started school before 2021-04-10
# easter until may:
"2021-04-11": generous_emergency_care_multiplier,
"2021-04-30": generous_emergency_care_multiplier,
# may
# we ignore Pentecoast vacation because it was <4 days on average
"2021-05-01": a_b_for_most_multiplier,
"2021-05-31": a_b_for_most_multiplier,
}
)
school_multiplier_with_vacations = get_piecewise_linear_interpolation(
school_multiplier_with_vacations
)
return school_multiplier_without_vacations, school_multiplier_with_vacations