Fit a contact model to a survey population

fits a gam model for each setting on the survey population data & the setting wise contact data. The underlying method is described in more detail in fit_single_contact_model(). The models can be fit in parallel, see the examples. Note that this function is parallelisable with future, and will be impacted by any future plans provided.

Usage

fit_setting_contacts(
  contact_data_list,
  population,
  symmetrical = TRUE,
  school_demographics = NULL,
  work_demographics = NULL
)

Arguments

contact_data_list

A list of dataframes, each containing information on the setting (home, work, school, other), age_from, age_to, the number of contacts, and the number of participants. Example data can be retrieved with get_polymod_setting_data().

population

conmat_population object or dataset with columns lower.age.limit and population. Example data can be retrieved with get_polymod_population().

symmetrical

whether to enforce symmetrical terms in the model. Defaults to TRUE. See details of fit_single_contact_model for more information.

school_demographics

(optional) defaults to census average proportion at school. You can provide a dataset with columns, "age" (numeric), and "school_fraction" (0-1), if you would like to specify these details. See abs_avg_school for the default values. If you would like to use the original school demographics used in conmat, these are provided in the dataset, conmat_original_school_demographics.

work_demographics

(optional) defaults to census average proportion employed. You can provide a dataset with columns, "age" (numeric), and "work_fraction", if you would like to specify these details. See abs_avg_work for the default values. If you would like to use the original work demographics used in conmat, these are provided in the dataset, conmat_original_work_demographics.

Value

list of fitted gam models - one for each setting provided

Author

Nicholas Tierney

Examples

# These aren't being  run as they take too long to fit
if (FALSE) {
contact_model <- fit_setting_contacts(
  contact_data_list = get_polymod_setting_data(),
  population = get_polymod_population()
)

# can fit the model in parallel
library(future)
plan(multisession, workers = 4)

polymod_setting_data <- get_polymod_setting_data()
polymod_population <- get_polymod_population()

contact_model <- fit_setting_contacts(
  contact_data_list = polymod_setting_data,
  population = polymod_population
)

# you can specify your own population data for school and work demographics
contact_model_diff_data <- fit_setting_contacts(
  contact_data_list = polymod_setting_data,
  population = polymod_population,
  school_demographics = conmat_original_school_demographics,
  work_demographics = conmat_original_work_demographics
)
}