src.create_initial_states.create_initial_immunity

Module Contents

Functions

create_initial_immunity(empirical_infections, synthetic_data, initial_infections, date, seed, reporting_delay, population_size)	Create a Series with initial immunity.
_calculate_total_immunity_prob(total_immunity, synthetic_data, population_size)	Calculate the probability to be immune by county and age group.
_calculate_endog_immunity_prob(initial_infections, synthetic_data)	Calculate the immunity probability from initial infections.
_calculate_exog_immunity_prob(total_immunity_prob, endog_immunity_prob)	Conditional probability to be immune, given not endogenously immune.

create_initial_immunity(empirical_infections, synthetic_data, initial_infections, date, seed, reporting_delay, population_size)

Create a Series with initial immunity.

Parameters

• empirical_infections (pandas.Series) – Newly infected Series with the index levels [“date”, “county”, “age_group_rki”]. These must already be corrected to include undetected cases.

• synthetic_data (pandas.DataFrame) – Dataset with one row per simulated individual. Must contain the columns age_group_rki and county.

• initial_infections (pandas.DataFrame) – DataFrame with same index as synthetic_data and one column for each day until date. Dtype is boolean. It is assumed that these already include undetected cases.

• seed (int) –

• reporting_delay (int) – Number of days by which the reporting of cases is delayed. If given, later days are used to get the infections of the demanded time frame.

• population_size (int) – Size of the population behind the empirical_infections.

Returns

Boolean series with same index as synthetic_data.

Return type

pd.Series

_calculate_total_immunity_prob(total_immunity, synthetic_data, population_size)

Calculate the probability to be immune by county and age group.

Parameters

• total_immunity (pandas.Series) – index are the county and age group. Values are the total numbers of immune individuals. These must already include undetected cases.

• synthetic_data (pandas.DataFrame) – DataFrame of synthetic individuals. Must contain age_group_rki and county as columns.

• population_size (int) – number of individuals in the population from which the total_immunity was calculated.

Returns

Index are county and age group

combinations. Values are the probabilities of individuals of a particular county and age group to be immune.

Return type

immunity_prob (pandas.Series)

_calculate_endog_immunity_prob(initial_infections, synthetic_data)

Calculate the immunity probability from initial infections.

Parameters

• initial_infections (pandas.DataFrame) – DataFrame with same index as synthetic_data and one column for each day between start and end. Dtype is boolean.

• synthetic_data (pandas.DataFrame) – Dataset with one row per simulated individual. Must contain the columns age_group_rki and county.

Returns

Probabilities

to become initially infected by age group and county.

Return type

prob_endog_immune (pandas.Series)

_calculate_exog_immunity_prob(total_immunity_prob, endog_immunity_prob)

Conditional probability to be immune, given not endogenously immune.