src.simulation.scenario_simulation_inputs¶
Build simulation inputs that depend on the scenario.
Every public function receives the dependency paths and fixed inputs as arguments and must return a dictionary with the following entries:
contact_policies
vaccination_models
rapid_test_models
rapid_test_reaction_models
Module Contents¶
Functions¶
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Increase the vaccination rate to that of the best average weekday. |
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Increase the vaccination rate to that of the best average weekday. |
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Increase the vaccination rate to 1% per day. |
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Change the inputs to remove knowledge of the time after March 1st. |
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Use an extrapolated prediction from February for the vaccination shares. |
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Define strict home office after Easter. |
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Define strict home office after summer scenario starts. |
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Set the attend work multiplier to new_attend_multiplier after date. |
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Hypothetical scenario where there is 10 pct less home office after Easter. |
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Hypothetical scenario where there is 10 pct more home office after Easter. |
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Multiply the attend work multiplier with multiplier after date. |
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- vaccinations_after_summer_scenario_start_as_on_strongest_week_day(paths, fixed_inputs)[source]¶
Increase the vaccination rate to that of the best average weekday.
Averages were taken over the time since family physicians started vaccinating. This vaccination rate is extrapolated to every day, including weekends.
This abstracts from possible constraints on the amount of available doses.
- vaccinations_after_easter_as_on_strongest_week_day(paths, fixed_inputs)[source]¶
Increase the vaccination rate to that of the best average weekday.
Averages were taken over the time since familiy physicians started vaccinating. This vaccination rate is extrapolated to every day, including weekends.
This abstracts from possible constraints on the amount of available doses.
- vaccinate_1_pct_per_day_after_easter(paths, fixed_inputs)[source]¶
Increase the vaccination rate to 1% per day.
There were 3 days on which this many people were vaccinated before May 26.
This abstracts from possible constraints on the amount of available doses.
- _get_vaccination_model_with_new_value_after_date(vaccination_shares, init_start, change_date, new_val, model_name)[source]¶
- robustness_check(paths, fixed_inputs)[source]¶
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.
Use an extrapolated prediction from February for the vaccination shares.
- strict_home_office_after_easter(paths, fixed_inputs)[source]¶
Define strict home office after Easter.
The attendance multiplier is set to 0.54 which corresponds to the mean work multiplier in April 2020.
- strict_home_office_after_summer_scenario_start(paths, fixed_inputs)[source]¶
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.
- _get_policies_with_different_work_attend_multiplier_after_date(enacted_policies, contact_models, new_attend_multiplier, split_date, prefix)[source]¶
Set the attend work multiplier to new_attend_multiplier after date.
In April 2020, the mean work multiplier was 0.54. In November 2020 it was 0.83.
- minus_10_pct_home_office_after_easter(paths, fixed_inputs)[source]¶
Hypothetical scenario where there is 10 pct less home office after Easter.
- plus_10_pct_home_office_after_easter(paths, fixed_inputs)[source]¶
Hypothetical scenario where there is 10 pct more home office after Easter.