Predict setting contacts — predict_setting

Predict contact rate for each setting. Note that this function is parallelisable with future, and will be impacted by any future plans provided.

Usage

predict_setting_contacts(
  population,
  contact_model,
  age_breaks,
  per_capita_household_size = NULL,
  model_per_capita_household_size = get_polymod_per_capita_household_size()
)

Arguments

population: population
contact_model: contact_model
age_breaks: age_breaks
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.
model_per_capita_household_size: modelled per capita household size. Default values for this are from get_polymod_per_capita_household_size(), which ends up being 3.248971

Value

List of setting matrices

Details

We use Per-capita household size instead of mean household size. Per-capita household size is different to mean household size, as the household size averaged over people in the population, not over households, so larger households get upweighted. It is calculated by taking a distribution of the number of households of each size in a population, multiplying the size by the household by the household count to get the number of people with that size of household, and computing the population-weighted average of household sizes. We use per-capita household size as it is a more accurate reflection of the average number of household members a person in the population can have contact with.

Author

Nicholas Tierney

Examples

# don't run as it takes too long to fit
# \donttest{
fairfield <- abs_age_lga("Fairfield (C)")
fairfield
#> # A tibble: 18 × 4 (conmat_population)
#>  - age: lower.age.limit
#>  - population: population
#>    lga           lower.age.limit  year population
#>    <chr>                   <dbl> <dbl>      <dbl>
#>  1 Fairfield (C)               0  2020      12261
#>  2 Fairfield (C)               5  2020      13093
#>  3 Fairfield (C)              10  2020      13602
#>  4 Fairfield (C)              15  2020      14323
#>  5 Fairfield (C)              20  2020      15932
#>  6 Fairfield (C)              25  2020      16190
#>  7 Fairfield (C)              30  2020      14134
#>  8 Fairfield (C)              35  2020      13034
#>  9 Fairfield (C)              40  2020      12217
#> 10 Fairfield (C)              45  2020      13449
#> 11 Fairfield (C)              50  2020      13419
#> 12 Fairfield (C)              55  2020      13652
#> 13 Fairfield (C)              60  2020      12907
#> 14 Fairfield (C)              65  2020      10541
#> 15 Fairfield (C)              70  2020       8227
#> 16 Fairfield (C)              75  2020       5598
#> 17 Fairfield (C)              80  2020       4006
#> 18 Fairfield (C)              85  2020       4240

age_break_0_85_plus <- c(seq(0, 85, by = 5), Inf)

polymod_contact_data <- get_polymod_setting_data()
polymod_survey_data <- get_polymod_population()

setting_models <- fit_setting_contacts(
  contact_data_list = polymod_contact_data,
  population = polymod_survey_data
)
#> Warning: fitted rates numerically 0 occurred
#> Warning: fitted rates numerically 0 occurred

synthetic_settings_5y_fairfield <- predict_setting_contacts(
  population = fairfield,
  contact_model = setting_models,
  age_breaks = age_break_0_85_plus
)

fairfield_hh_size <- get_abs_per_capita_household_size(lga = "Fairfield (C)")
fairfield_hh_size
#> [1] 4.199372

synthetic_settings_5y_fairfield_hh <- predict_setting_contacts(
  population = fairfield,
  contact_model = setting_models,
  age_breaks = age_break_0_85_plus,
  per_capita_household_size = fairfield_hh_size
)
# }