# Predict contact rate between two age populations, given some model.

`predict_contacts.Rd`

Predicts the expected contact rate over specified age breaks,
given some model of contact rate and population age structure.
This function is used internally in `predict_setting_contacts()`

, which
performs this prediction across all settings (home, work, school, other),
and optionally performs an adjustment for per capita household size. You
can use `predict_contacts()`

by itself, just be aware you will need to
separately apply a per capita household size adjustment if required. See
details below on `adjust_household_contact_matrix`

for more information.

## 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_breaks
the ages to predict to. By default, the age breaks are 0-75 in 5 year groups.

## Value

A dataframe with three columns: `age_group_from`

, `age_group_to`

,
and `contacts`

. The age groups are factors, broken up into 5 year bins
`[0,5)`

, `[5,10)`

. The `contact`

column is the predicted number of
contacts from the specified age group to the other one.

## Details

The population data is used to determine age range to predict
contact rates, and removes ages with zero population, so we do not
make predictions for ages with zero populations. Contact rates are
predicted yearly between the age groups, using `predict_contacts_1y()`

,
then aggregates these predicted contacts using
`aggregate_predicted_contacts()`

, which aggregates the predictions back to
the same resolution as the data, appropriately weighting the contact rate
by the population.

Regarding the `adjust_household_contact_matrix`

function, 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.

## Examples

```
# If we have a model of contact rate at home, and age population structure
# for an LGA, say, Fairfield, in NSW:
polymod_setting_models$home
#>
#> Family: poisson
#> Link function: log
#>
#> Formula:
#> contacts ~ s(gam_age_offdiag) + s(gam_age_offdiag_2) + s(gam_age_diag_prod) +
#> s(gam_age_diag_sum) + s(gam_age_pmax) + s(gam_age_pmin) +
#> school_probability + work_probability + offset(log_contactable_population)
#>
#> Estimated degrees of freedom:
#> 7.74 1.00 6.29 2.96 8.50 3.10 total = 32.6
#>
#> fREML score: 14701.28 rank: 55/57
fairfield_abs_data <- abs_age_lga("Fairfield (C)")
fairfield_abs_data
#> # A tibble: 18 × 4 (conmat_population)
#> - age: lower.age.limit
#> - population: population
#> lga lower.age.limit year population
#> <chr> <dbl> <dbl> <dbl>
#> 1 Fairfield (C) 0 2020 12261
#> 2 Fairfield (C) 5 2020 13093
#> 3 Fairfield (C) 10 2020 13602
#> 4 Fairfield (C) 15 2020 14323
#> 5 Fairfield (C) 20 2020 15932
#> 6 Fairfield (C) 25 2020 16190
#> 7 Fairfield (C) 30 2020 14134
#> 8 Fairfield (C) 35 2020 13034
#> 9 Fairfield (C) 40 2020 12217
#> 10 Fairfield (C) 45 2020 13449
#> 11 Fairfield (C) 50 2020 13419
#> 12 Fairfield (C) 55 2020 13652
#> 13 Fairfield (C) 60 2020 12907
#> 14 Fairfield (C) 65 2020 10541
#> 15 Fairfield (C) 70 2020 8227
#> 16 Fairfield (C) 75 2020 5598
#> 17 Fairfield (C) 80 2020 4006
#> 18 Fairfield (C) 85 2020 4240
# We can predict the contact rate for Fairfield from the existing contact
# data, say, between the age groups of 0-15 in 5 year bins for school:
fairfield_school_contacts <- predict_contacts(
model = polymod_setting_models$school,
population = fairfield_abs_data,
age_breaks = c(0, 5, 10, 15, Inf)
)
fairfield_school_contacts
#> # A tibble: 16 × 3
#> age_group_from age_group_to contacts
#> <fct> <fct> <dbl>
#> 1 [0,5) [0,5) 1.20
#> 2 [0,5) [5,10) 0.335
#> 3 [0,5) [10,15) 0.0555
#> 4 [0,5) [15,Inf) 0.630
#> 5 [5,10) [0,5) 0.319
#> 6 [5,10) [5,10) 4.42
#> 7 [5,10) [10,15) 0.395
#> 8 [5,10) [15,Inf) 1.26
#> 9 [10,15) [0,5) 0.0499
#> 10 [10,15) [5,10) 0.378
#> 11 [10,15) [10,15) 6.59
#> 12 [10,15) [15,Inf) 1.71
#> 13 [15,Inf) [0,5) 0.0449
#> 14 [15,Inf) [5,10) 0.0945
#> 15 [15,Inf) [10,15) 0.136
#> 16 [15,Inf) [15,Inf) 1.07
```