DiseasyPopulation

library(diseasy)
#> Loading required package: diseasystore
#> 
#> Attaching package: 'diseasy'
#> The following object is masked from 'package:diseasystore':
#> 
#>     diseasyoption

Overview

The DiseasyPopulation module is responsible for defining the model population and interfacing with the models.

Presently, DiseasyPopulation combines DiseasyRegions and DiseasyActivity to configure a model population stratified by age and geographical region. Where DiseasyRegions defines the scope and possible stratifications of the model population, DiseasyPopulation manages the stratification itself. Similarly, DiseasyActivity defines the age-specific interactions between age groups as well as age-specific societal restrictions over time, while DiseasyPopulation aggregates these effects into user stratified age groups. Combined, this forms a model population stratified both spatially and by age groups.

Defining the population of interest

The first step of defining the model population is to define a population of interest.

For DiseasyPopulation, this is done via DiseasyRegions[^1] which handles demography data (see vignette(diseasy-regions) for more details).

[^1] Or, as in this case, DiseasyRegionsNuts which is a generalisation of DiseasyRegions.

regions <- DiseasyRegionsNuts$new(
  demography = demography_nordic_nuts3, # Use demography data from EUROSTAT
  adjacency = adjacency_meta_nordic_nuts, # Use adjacency data from Meta
  area = "DK" # Restrict population of interest to Danish population
)

regions
#> # DiseasyRegions #############################################
#> Area: DK
#> Total population: 5,992,734
#> Theta matrix: Max eigenvalue 1.07

Once the scope of the population has been defined in DiseasyRegions, this can be passed to DiseasyPopulation to handle the remaining configuration of the model population.

population <- DiseasyPopulation$new(regions = regions)

population
#> # DiseasyPopulation ##########################################
#> Stratifications:
#> Age: No age stratification has been configured
#> Space: No spatial stratification has been configured

Defining the age-specific interactions

If the model population should be stratified by age, the next step is to configure the age-specific interactions between age groups (contact matrices). In diseasy, this is done via the DiseasyActivity¹.

activity <- DiseasyActivity$new(
  contact_basis = contact_basis_nordic %.% DK
)

activity
#> # DiseasyActivity ############################################
#> Scenario: Activity scenario not yet set
#> Contact basis: Contact matrices for Denmark from the `contactdata` package and population data for
#> Denmark from the US Census Bureau.

… and, once a basis of contact matrices has defined, the DiseasyActivity can be passed to DiseasyPopulation.

population$load_module(activity)

population
#> # DiseasyPopulation ##########################################
#> Stratifications:
#> Age: No age stratification has been configured
#> Space: No spatial stratification has been configured

Stratifying to form the model population

With the population basis defined via DiseasyRegions and DiseasyActivity, the model population can now be stratified at various resolutions to before being passed to the models.

To stratify by age, we use the $stratify_age() method:

population$stratify_age(age_cuts_lower = c(0, 20, 40, 60, 80))

# and we can inspect the defined demographic groups
population$group
#> NULL

To stratify by region, we use the $stratify_regions() method:

population$stratify_regions(regional_stratification = "NUTS 2")

# .. and we can inspect the updated demographic groups
population$group
#> NULL

Populations outside the Nordics

To keep the installation size of diseasy light, we only bundle population data for the Nordic countries.

To generate data for other regions, see the data generating functions: generate_demography(), generate_contact_basis(), etc.