Modelling multiple contact settings

This vignette shows how to pass community contact matrices disaggregated into multiple settings to the Daedalus model.

The idea is — in future — to be able to apply closures or any arbitrary scaling to each setting separately, rather than uniformly scaling contacts across settings. An example of setting-specific scaling is scaling school contacts for school-age children, while leaving contacts in other settings intact.

Note that While the scaffolding for such scaling functionality is in place on the C++ side, this functionality is still being developed on the R side.

Basic example using POLYMOD

This example using POLYMOD data (Mossong et al. 2008) shows how to assign contacts from two different settings to a <daedalus_country> object.

Once this assignment is done, helper functions such as prepare_parameters() take care of expanding and scaling the contact matrices when the country object is passed to daedalus().

Note that running daedalus("XYZ", ...) cannot be used to model multiple settings, and the country argument must be a <daedalus_country> if multiple settings are needed.

# load package
library(daedalus)
library(socialmixr) # for POLYMOD data

By default a country has a single setting, the ‘total’ community contacts. This is displayed in the country object when printed to screen.

daedalus_country("GBR")
#> <daedalus_country>
#> • Name: United Kingdom
#> • Demography: 3924490, 11762039, 39536463, and 12663012
#> • Default contact matrix:
#> • * setting name: "total"; found no more settings
#>             0-4      5-19    20-64       65+
#> 0-4   1.9157895 1.5379290 4.704999 0.2863619
#> 5-19  0.5131412 8.7339228 5.874591 0.7418483
#> 20-64 0.4670302 1.7476822 7.830182 1.0685802
#> 65+   0.1180517 0.7548304 3.531487 1.5212437
#> • GNI (PPP $): 45870
#> • Hospital capacity: 26219

We can get POLYMOD data (Mossong et al. 2008) for the U.K. from the R package socialmixr. Here in this basic example, we consider only two settings, ‘home’ and ‘school’.

# load polymod data and get home and school contacts
data(polymod)

# get matrices with Daedalus age bins
cm_uk_home <- contact_matrix(
  polymod,
  "GB",
  age_limits = c(0, 5, 18, 65),
  filter = list(cnt_home = 1)
)$matrix

cm_uk_school <- contact_matrix(
  polymod,
  "GB",
  age_limits = c(0, 5, 18, 65),
  filter = list(cnt_school = 1)
)$matrix

Create a new country object, passing the new settings to the contact_matrix argument as a named list. Having names helps when examining the object later.

x <- daedalus_country(
  "GB",
  contact_matrix = list(
    home = cm_uk_home,
    school = cm_uk_school
  )
)

# print x to see output
x
#> <daedalus_country>
#> • Name: United Kingdom
#> • Demography: 3924490, 11762039, 39536463, and 12663012
#> • Default contact matrix:
#> • * setting name: "home"; found 1 more setting: "school"
#>          contact.age.group
#> age.group     [0,5)    [5,18)  [18,65)       65+
#>   [0,5)   0.4842105 1.0631579 2.694737 0.1157895
#>   [5,18)  0.3011152 1.7397770 2.516729 0.1449814
#>   [18,65) 0.2656514 0.9323181 2.218274 0.2081218
#>   65+     0.1250000 0.7142857 1.660714 0.7142857
#> • GNI (PPP $): 45870
#> • Hospital capacity: 26219

Pass the created country to [daedalus()] as usual to run the epi-econ model taking into account contacts in each setting.

daedalus(x, "sars_cov_1", time_end = 100)
#> <daedalus_output>
#> • Country: United Kingdom
#> • Epidemic: sars_cov_1
#> • NPI response: none
#> • Vaccination: no vaccination
#> • Behaviour: no behaviour

Future developments

A number of developments to this feature are being considered. These include:

1. Disaggregating workplace contacts into settings, so that scaling can be applied by setting rather than by economic sector, as this is much easier to work with from a policy perspective (e.g. closing pubs versus scaling hospitality contacts by some amount to represent pub closures).

2. Applying age-specific setting scaling, to represent different policy recommendations or outcomes by age in different settings.

3. Applying different behavioural modifiers to each setting to simulate endogenous risk-perception related modification in contacts across settings.

References

Mossong, Joël, Niel Hens, Mark Jit, Philippe Beutels, Kari Auranen, Rafael Mikolajczyk, Marco Massari, et al. 2008. “Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases.” PLOS Medicine 5 (3): e74. https://doi.org/10.1371/journal.pmed.0050074.