Predict contact rate to a given population at full 1y resolution

Provides a predicted rate of contacts for contact ages. Take an already fitted model of contact rate and predict the estimated contact rate, and standard error, for all combinations of the provided ages in 1 year increments. So if the minimum age is 5, and the maximum age is 10, it will provide the estimated contact rate for all age combinations: 5 and 5, 5 and 6 ... 5 and 10, and so on. This function is used internally within predict_contacts(), and thus predict_setting_contacts() as well, although it can be used by itself. See examples for more details, and details for more information.

Usage

predict_contacts_1y(model, population, age_min = 0, age_max = 100)

Arguments

model: A single fitted model of contact rate (e.g., fit_single_contact_model())
population: a dataframe of age population information, with columns indicating some lower age limit, and population, (e.g., get_polymod_population())
age_min: Age range minimum value. Default: 0
age_max: Age range maximum value, Default: 100

Value

Data frame with four columns: age_from, age_to, contacts, and se_contacts. This contains the participant & contact ages from the minimum and maximum ages provided along with the predicted rate of contacts and standard error around the prediction.

Details

Prediction features are added using add_modelling_features(). These features include the population distribution of contact ages, fraction of population in each age group that attend school/work as well as the offset according to the settings on all combinations of the participant & contact ages.

Examples


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

# predict the contact rates in 1 year blocks to Fairfield data

fairfield_contacts_1 <- predict_contacts_1y(
  model = polymod_setting_models$home,
  population = fairfield,
  age_min = 0,
  age_max = 2
)