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Get predicted setting specific as well as combined contact matrices

estimate_setting_contacts.Rd

Given a named list of contact datasets (with names giving the setting, and assumed to together make up the full set of contacts for individuals in the survey), a representative population distribution for the survey, and a set of age breaks at which to aggregate contacts, return a set of predicted contact matrices for each setting, and for all combined. Note that this function is parallelisable with future, and will be impacted by any future plans provided.

Usage

estimate_setting_contacts(
  contact_data_list,
  survey_population,
  prediction_population = survey_population,
  age_breaks,
  per_capita_household_size = NULL,
  symmetrical = TRUE,
  school_demographics = NULL,
  work_demographics = NULL
)

Arguments

contact_data_list

list of data sets with information on the contacts of individuals at different settings

survey_population

representative population distribution for the survey

prediction_population

population for prediction. The default value set is survey_population

age_breaks

vector depicting age values. For example, c(seq(0, 75, by = 5), Inf)

per_capita_household_size

Optional (defaults to NULL). When set, it adjusts the household contact matrix by some per capita household size. To set it, provide a single number, the per capita household size. More information is provided below in Details. See get_abs_per_capita_household_size() function for a helper for Australian data with a workflow on how to get this number.

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

predicted setting specific contact matrices, and for all combined

Examples

if (FALSE) {
# takes a long time to run
settings_estimated_contacts <- estimate_setting_contacts(
  contact_data_list = get_polymod_setting_data(),
  survey_population = get_polymod_population(),
  prediction_population = get_polymod_population(),
  age_breaks = c(seq(0, 85, by = 5), Inf),
  per_capita_household_size = NULL
)

# or predict to fairfield
fairfield_hh <- get_abs_per_capita_household_size(lga = "Fairfield (C)")
contact_model_pred_est <- estimate_setting_contacts(
  contact_data_list = get_polymod_setting_data(),
  survey_population = get_polymod_population(),
  prediction_population = abs_age_lga("Fairfield (C)"),
  age_breaks = c(seq(0, 85, by = 5), Inf),
  per_capita_household_size = fairfield_hh
)

# or use different populations in school or work demographics
fairfield_hh <- get_abs_per_capita_household_size(lga = "Fairfield (C)")
contact_model_pred_est <- estimate_setting_contacts(
  contact_data_list = get_polymod_setting_data(),
  survey_population = get_polymod_population(),
  prediction_population = abs_age_lga("Fairfield (C)"),
  age_breaks = c(seq(0, 85, by = 5), Inf),
  per_capita_household_size = fairfield_hh,
  school_demographics = conmat_original_school_demographics,
  work_demographics = conmat_original_work_demographics
)

# or use non-symmetric model terms
contact_model_pred_est <- estimate_setting_contacts(
  contact_data_list = get_polymod_setting_data(),
  survey_population = get_polymod_population(),
  prediction_population = abs_age_lga("Fairfield (C)"),
  age_breaks = c(seq(0, 85, by = 5), Inf),
  per_capita_household_size = fairfield_hh,
  symmetrical = FALSE
)
}