src.simulation.params_scenarios

Module Contents

Functions

baseline(params)
no_rapid_tests_at_schools(params)
no_rapid_tests_at_work(params)
no_private_rapid_test_demand(params)
no_rapid_tests_at_schools_and_work(params)
no_rapid_tests_at_schools_and_private(params)
no_rapid_tests_at_work_and_private(params)
no_rapid_tests_at_schools_after_easter(params)
rapid_tests_at_school_every_other_day_after_april_5(params)
rapid_tests_at_school_every_day_after_april_5(params)
no_seasonality(params)	Set the seasonality to 1 everywhere.
start_all_rapid_tests_after_easter(params)	Start all rapid tests with full force after Easter instead of fading them in.
keep_work_offer_share_at_23_pct_after_easter(params)	Set work offer share to 23 percent (March 17th value) after Easter.
mandatory_work_rapid_tests_after_easter(params)	Assume work rapid tests are nearly universal after Easter.
robustness_check_params_early(params)
robustness_check_params_medium(params)
robustness_check_params_late(params)
_robustness_check_params(params, date)	Remove drop in share_known_cases for Easter and simplify rapid test demand.
_rapid_test_with_fixed_compliance_after_date(params, change_date, new_val)	Implement a rapid test scheme where a certain share of workers get tested.
_set_to_multiple_of_work_rapid_test_demand_after_date_start(params, date, multiplier)	Have a constant multiplier x old work rapid test demand after date.
_change_piecewise_linear_parameter_to_fixed_value_after_date(params, loc, change_date, new_val)	Change piecewise linear params to be constant at a new value after a certain date.
_build_new_date_params(before_params_slice, change_date, new_val)

baseline(params)

no_rapid_tests_at_schools(params)

no_rapid_tests_at_work(params)

no_private_rapid_test_demand(params)

no_rapid_tests_at_schools_and_work(params)

no_rapid_tests_at_schools_and_private(params)

no_rapid_tests_at_work_and_private(params)

no_rapid_tests_at_schools_after_easter(params)

rapid_tests_at_school_every_other_day_after_april_5(params)

rapid_tests_at_school_every_day_after_april_5(params)

no_seasonality(params)

Set the seasonality to 1 everywhere.

This induces a jump in the seasonality compared to scenarios with seasonality almost everywhere.

start_all_rapid_tests_after_easter(params)

Start all rapid tests with full force after Easter instead of fading them in.

keep_work_offer_share_at_23_pct_after_easter(params)

Set work offer share to 23 percent (March 17th value) after Easter.

mandatory_work_rapid_tests_after_easter(params)

Assume work rapid tests are nearly universal after Easter.

We assume both 5% refusers on side of firms and 5% on the side of employees. Thus, effectively ~90% of workers get tested.

robustness_check_params_early(params)

robustness_check_params_medium(params)

robustness_check_params_late(params)

_robustness_check_params(params, date)

Remove drop in share_known_cases for Easter and simplify rapid test demand.

_rapid_test_with_fixed_compliance_after_date(params, change_date, new_val)

Implement a rapid test scheme where a certain share of workers get tested.

_set_to_multiple_of_work_rapid_test_demand_after_date_start(params, date, multiplier)

Have a constant multiplier x old work rapid test demand after date.

Since only the offer share of workers’ rapid tests is time variant we change the offer parameters rather than the demand even though the more intuitive interpretation would be that workers demand less tests. Where the reduction takes place is irrelevant because offer and demand shares are multiplied to get the overall work rapid share multiplier.

Parameters

• params (pandas.DataFrame) – params DataFrame with (“rapid_test_demand”, “share_workers_receiving_offer”) entries.

• date (str or pandas.Timestamp) – date after which the workers’ rapid tests are reduced.

• multiplier (float) – multiplier with which the old work rapid test share will be reduced. For example, to reduce the work rapid tests by 25%, set the multiplier to 0.75.

_change_piecewise_linear_parameter_to_fixed_value_after_date(params, loc, change_date, new_val)

Change piecewise linear params to be constant at a new value after a certain date.

This function can be used to change any params that are fed to get_piecewise_linear_interpolation.

The resulting piecewise linear interpolation is the same until change_date and then falls on change_date to the new value and stays there.

Parameters

• params (pandas.DataFrame) –

• loc (tuple) – tuple of length two, identifying the slice of time variant parameters to be changed.

• change_date (str or pandas.Timestamp) – date from which on the parameter will take the new value.

• new_val (float) – the new value which the parameter will take after change_date.

Returns

the full params that were passed to this function

with the loc exchanged for the new time variant parameters.

Return type

params (pandas.DataFrame)

_build_new_date_params(before_params_slice, change_date, new_val)