Function reference • conmat

Core functions

These are the core functions that we intend users to interface with

extrapolate_polymod(): Fit all-of-polymod model and extrapolate to a given population an age breaks

apply_vaccination(): Apply vaccination effects to next generation contact matrices

generate_ngm(): Calculate next generation contact matrices

generate_ngm_oz(): Calculate next generation contact matrices from ABS data

reexports autoplot: Objects exported from other packages

autoplot(<conmat_age_matrix>) autoplot(<conmat_setting_prediction_matrix>) autoplot(<transmission_probability_matrix>) autoplot(<ngm_setting_matrix>) autoplot(<setting_vaccination_matrix>): Plot setting matrices using ggplot2

ABS Data accessors and helpers

Functions and data objects to access data from the Australian Bureau of Statistics

abs_age_education_state() abs_age_education_lga(): Return data on educated population for a given age and state or lga of Australia.

abs_age_work_lga() abs_age_work_state(): Return data on employed population for a given age and state or lga of Australia

abs_abbreviate_states(): Abbreviate Australian State Names

abs_age_lga() abs_age_state(): Return Australian Bureau of Statistics (ABS) age population data for a given Local Government Area (LGA) or state

abs_avg_school: ABS education data for 2016

abs_avg_work: ABS work data for 2016

abs_education_state: ABS education by state for 2006-2020

abs_education_state_2020: 2020 ABS education population data, interpolated into 1 year bins, by state.

abs_employ_age_lga: ABS employment by age and LGA for 2016

abs_household_lga: ABS household data for 2016

abs_lga_lookup: ABS lookup table of states, lga code and lga name

abs_pop_age_lga_2016: ABS population by age for 2016 for LGAs

abs_pop_age_lga_2020: ABS population by age for 2020 for LGAs

abs_state_age: ABS state population data for 2020

abs_unabbreviate_states(): Un-abbreviate Australian state names

data_abs_lga_education: LGA wise ABS education population data on different ages for year 2016

data_abs_lga_work: LGA wise ABS work population data on different ages for year 2016

data_abs_state_education: State wise ABS education population data on different ages for year 2016

data_abs_state_work: State wise ABS work population data on different ages for year 2016

get_abs_household_size_distribution(): Get household size distribution based on state or LGA name

get_abs_household_size_population(): Get population associated with each household size in an LGA or a state

get_abs_per_capita_household_size(): Get per capita household size based on state or LGA name

get_abs_per_capita_household_size_lga(): Get household size distribution based on LGA name

get_abs_per_capita_household_size_state(): Get household size distribution based on state name

Data and model objects

Functions and objects for accessing data, models, and assisting with model fitting.

get_polymod_contact_data(): Format POLYMOD data and filter contacts to certain settings

get_polymod_per_capita_household_size(): Get polymod per capita household size.

get_polymod_population(): Return the polymod-average population age distribution in 5y

get_polymod_setting_data(): Get polymod setting data

age_population(): Get cleaned population data with lower and upper limits of age.

vaccination_effect_example_data: Example dataset with information on age based vaccination coverage, acquisition and transmission

polymod_setting_models: Polymod Settings models

davies_age_extended: Susceptibility and clinical fraction parameters from Davies et al.

eyre_transmission_probabilities: Transmission probabilities of COVID19 from Eyre et al.

polymod: Social contact data from 8 European countries (imported from socialmixr)

setting_weights: Setting weights computed for transmission probabilities.

age_group_lookup: Lookup table of age groups in 5 year bins

get_setting_transmission_matrices(): Get Setting Transmission Matrices

prem_germany_contact_matrices: Contact matrices as calculated by Prem. et al.

conmat_original_school_demographics: Original school demographics for conmat

conmat_original_work_demographics: Original work demographics for conmat

abs_avg_school: ABS education data for 2016

abs_avg_work: ABS work data for 2016

Extra functions for model fitting and prediction

Functions for model fitting and predictions - for more advanced use

fit_single_contact_model(): Fit a single GAM contact model to a dataset

fit_setting_contacts(): Fit a contact model to a survey population

aggregate_predicted_contacts(): Aggregate predicted contacts to specified age breaks

estimate_setting_contacts(): Get predicted setting specific as well as combined contact matrices

predict_contacts(): Predict contact rate between two age populations, given some model.

predict_contacts_1y(): Predict contact rate to a given population at full 1y resolution

predict_setting_contacts(): Predict setting contacts

predictions_to_matrix(): Convert dataframe of predicted contacts into matrix

matrix_to_predictions(): Convert a contact matrix as output into a long-form tibble

get_age_population_function(): Return an interpolating function for populations in 1y age increments

per_capita_household_size(): Get per capita household size with household size distribution

add_intergenerational(): Add column, "intergenerational"

add_modelling_features(): Add features required for modelling to the dataset

add_offset(): Adds offset variables

add_population_age_to(): Add the population distribution for contact ages.

add_school_work_participation(): Add columns describing the fractions of the population in each age group that attend school/work (average FTE)

add_symmetrical_features(): Add symmetrical, age based features

Creating and accessing information on `conmat_population` objects

Store and use age and population information in a dataframe for use in other functions in conmat.

conmat_population(): Define a conmat population

as_conmat_population(): Convert to conmat population

age() age_label() population_label() population(): Accessing conmat attributes

age_breaks(): Extract age break attribute information

raw_eigenvalue(): Get raw eigvenvalue from NGM matrix

scaling(): Get the scaling from NGM matrix

new_age_matrix(): Build new age matrix

setting_prediction_matrix(): Create a setting prediction matrix

as_setting_prediction_matrix(): Coerce object to a setting prediction matrix

transmission_probability_matrix(): Create a setting transmission matrix