Convert a contact matrix to per-capita rates

Divides each column of the contact matrix by the population of the contacted group, so that entry (a, b) becomes the mean number of contacts a member of group a makes with a single individual of group b. Multi-grouping matrices are handled the same way, with each combination of grouping levels treated as a group.

Usage

per_capita(x, survey_pop)

Arguments

x

a list as returned by compute_matrix(), with elements matrix and participants

survey_pop

a data frame; see Population data below

Value

x with $matrix replaced by the per-capita version

Population data

survey_pop is a data frame with one column per grouping, named after the grouping (e.g. age, gender) and holding that grouping's levels as they appear in the matrix, plus a population column with the size of each combination. One row per combination of levels is required, and levels are matched to the matrix exactly, without interpolation.

Use align_ages() to build this from a raw population table: it aggregates each grouping to the matrix's levels (interpolating the age grouping where needed) and labels the columns to match.

Examples

data(polymod)
result <- polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix()
uk_pop <- data.frame(
  age = limits_to_agegroups(0:80, notation = "brackets"),
  population = rep(1e5, 81)
)
result |> per_capita(survey_pop = align_ages(uk_pop, result))
#> 
#> ── Contact matrix (3 age groups) ──
#> 
#> Ages: "[0,5)", "[5,15)", and "[15,Inf)"
#> Participants: 1011
#> 
#>           contact.age.group
#> age.group         [0,5)       [5,15)     [15,Inf)
#>   [0,5)    3.831579e-06 1.431579e-06 8.373206e-07
#>   [5,15)   1.058824e-06 7.946078e-06 9.417706e-07
#>   [15,Inf) 7.808989e-07 1.290730e-06 1.453652e-06