In njtierney/conmat: Builds Contact Matrices using GAMs and Population Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

We provide access to a variety of different data sources in conmat. Most of these are centred around Australian data, as the package was initially created for disease modelling work in Australia. The aim of this vignette is to give a quick tour of the data sources available in conmat.

library(conmat)

World data

We provide functions to clean up world population data from socialmixr.

world_data <- socialmixr::wpp_age()

head(world_data)

We can tidy the data up, filtering down to a specified location and year with the age_population function:

nz_2015 <- age_population(
  data = world_data,
  location_col = country,
  location = "New Zealand",
  age_col = lower.age.limit,
  year_col = year,
  year = 2015
)

nz_2015

This returns a conmat_population object, which is a data frame that knows which columns represent age and population information. This is useful for other modelling parts of the conmat package.

Australian Bureau of Statistics (ABS) data

Accessing Functions

We provide two functions to access LGA (Local Government Area), and state level population age data, which are provided in 5 year age bins from 0, 5, up to 85+. These data are conmat_population tibbles, which means that they know which columns represent the age and population information. This means that functions inside of conmat can work a bit smoother as we refer to these columns frequently.

abs_age_lga()

fairfield <- abs_age_lga(lga_name = "Fairfield (C)")
fairfield

Note that this is a conmat_population object, which prints in red at the top of the data frame. This provides the information on the age and population columns, stating: age: lower.age.limit, and population: population, indicating which columns refer to the appropriate variables.

Also note that abs_age_lga requires you to know the exact name of the LGA, you can see them in the dataset, abs_lga_lookup

abs_lga_lookup

And if you're not sure about a particular name of a place, you can use agrep and filter, to match on similar-ish characters, like so:

library(dplyr)
abs_lga_lookup %>%
  filter(agrepl("Sydney", lga))

abs_age_state()

This takes in the abbreviated state names, and is also a conmat_population object.

abs_age_state(state_name = "NSW")

You can see these state names with:

unique(abs_lga_lookup$state)

Note that "OT" stands for "other territories"

ABS data

We provide other ABS data, listed now. You can read the full details of the data at their respective helpfiles, by writing, for example, ?abs_education_state.

Education by state data for 2006 -2020

abs_education_state

Education by state data for 2020

abs_education_state_2020

Employment by LGA for 2016

abs_employ_age_lga

Number of people in each household by LGA for 2016

abs_household_lga

LGA age population for 2016 for all states and LGAs

abs_pop_age_lga_2016

LGA age population for 2020 for all states and LGAs

abs_pop_age_lga_2020

State age population for 2020

abs_state_age

Epidemiology / disease modelling data

Transmission probabilities from Eyre

A dataset containing data digitised from "The impact of SARS-CoV-2 vaccination on Alpha & Delta variant transmission", by David W Eyre, Donald Taylor, Mark Purver, David Chapman, Tom Fowler, Koen B Pouwels, A Sarah Walker, Tim EA Peto

eyre_transmission_probabilities

We can visualise the data like so:

library(ggplot2)
library(stringr)
library(dplyr)
eyre_transmission_probabilities %>%
  group_by(
    setting,
    case_age_5y,
    contact_age_5y
  ) %>%
  summarise(
    across(
      probability,
      mean
    ),
    .groups = "drop"
  ) %>%
  rename(
    case_age = case_age_5y,
    contact_age = contact_age_5y
  ) %>%
  mutate(
    across(
      ends_with("age"),
      ~ factor(.x,
        levels = str_sort(
          unique(.x),
          numeric = TRUE
        )
      )
    )
  ) %>%
  ggplot(
    aes(
      x = case_age,
      y = contact_age,
      fill = probability
    )
  ) +
  facet_wrap(~setting) +
  geom_tile() +
  scale_fill_viridis_c() +
  coord_fixed() +
  theme_minimal() +
  theme(
    axis.text = element_text(angle = 45, hjust = 1)
  )

njtierney/conmat documentation built on April 17, 2025, 10:27 p.m.