Reassessment Data¶

leap.data_generation.reassessment_data module¶

leap.data_generation.reassessment_data.get_asthma_df(starting_year: int = 1999, max_year: int = 2065, min_age: int = 3, max_age: int = 110, max_asthma_age: int = 62, stabilization_year: int = 2025) → pandas.core.frame.DataFrame[source]¶

Loads the asthma prevalence / incidence predictions from Model 1.

Parameters:¶

starting_year: int = 1999¶: The starting year for the dataframe.
max_year: int = 2065¶: The ending year for the dataframe.
min_age: int = 3¶: The minimum age for asthma prediction.
max_age: int = 110¶: The maximum age for asthma prediction.
max_asthma_age: int = 62¶: The maximum age for for which the asthma prevalence / incidence model can accurately make predictions.
stabilization_year: int = 2025¶: The year when asthma stabilization occurs.

Returns:¶

A DataFrame containing asthma occurrence predictions. Columns:

age (int): age in years, range [min_age, max_age].
sex (str): one of "M" or "F".
year (int): calendar year, range [starting_year, max_year].
incidence (float): predicted asthma incidence for the given age, sex, and year.
prevalence (float): predicted asthma prevalence for the given age, sex, and year.

leap.data_generation.reassessment_data.calculate_reassessment_probability(prevalence_past: float, prevalence_current: float, incidence_current: float) → float[source]¶

Calculates the reassessment probability based on asthma prevalence and incidence.

Parameters:¶

prevalence_past: float¶: The prevalence of asthma from the previous year.
prevalence_current: float¶: The prevalence of asthma in the current year.
incidence_current: float¶: The incidence of asthma in the current year.

Returns:¶

The probability that someone diagnosed with asthma will maintain their diagnosis in the current year.

leap.data_generation.reassessment_data.get_reassessment_data(df_asthma: pandas.core.frame.DataFrame, province: str = 'CA', starting_year: int = 1999, max_year: int = 2065, max_age: int = 110) → pandas.core.frame.DataFrame[source]¶

Generates reassessment data for asthma prevalence and incidence.

Parameters:¶

df_asthma: pandas.core.frame.DataFrame¶

A dataframe containing asthma prevalence and incidence predictions from Occurrence Model 1. The dataframe should have the following columns:

age (int): age in years, range [3, max_age].
sex (str): one of "M" or "F".
year (int): calendar year, range [starting_year, max_year].
incidence (float): predicted asthma incidence for the given age, sex, and year.
prevalence (float): predicted asthma prevalence for the given age, sex, and year.

province: str = 'CA'¶

The 2-letter province code, e.g. "CA".

starting_year: int = 1999¶

The starting year for the data.

max_year: int = 2065¶

The ending year for the data.

max_age: int = 110¶

The maximum age for asthma prediction.

Returns:¶

A DataFrame containing the reassessment data. Columns:

year (int): calendar year, range [starting_year + 1, max_year].
province (str): the 2-letter province code, e.g. "CA".
age (int): age in years, range [4, max_age].
sex (str): one of "M" or "F".
prob (float): the probability that someone diagnosed with asthma will maintain their asthma diagnosis in the given year. Range: [0, 1].

leap.data_generation.reassessment_data.generate_reassessment_data()[source]¶: Generate reassessment data for asthma prevalence and incidence across different provinces.