Calculate next generation contact matrices

Once infected, a person can transmit an infectious disease to another, creating generations of infected individuals. We can define a matrix describing the number of newly infected individuals in given categories, such as age, for consecutive generations. This matrix is called a "next generation matrix" (NGM).

Usage

generate_ngm(state_name = NULL, lga_name = NULL, age_breaks, R_target)

Arguments

state_name: target Australian state name in abbreviated form, such as "QLD", "NSW", or "TAS"
lga_name: target Australian local government area (LGA) name, such as "Fairfield (C)". See abs_lga_lookup() for list of lga names.
age_breaks: vector depicting age values with the highest age depicted as Inf. For example, c(seq(0, 85, by = 5), Inf)
R_target: target reproduction number

Details

The NGM can be used to calculate the expected number of secondary infections in a given age group. Given certain age breaks, we compute the unscaled next generation matrices for that location across different settings & age groups using the contact rates extrapolated from POLYMOD survey data on the specified location, adjusted by the per capita household size and the setting-specific relative per-contact transmission probability matrices for the same age groups. These NGMs are then scaled according to a target reproduction number (which is provided as an argument) using the ratio of the desired R0 and the R0 of the NGM for the combination of all settings. The R0 of the combination of all settings is obtained by calculating the unique, positive eigen value of the combination NGM. This ratio is then used to scale all the setting specific NGMs.

Examples

# don't run as both together takes a long time to run
if (FALSE) {
ngm_nsw <- generate_ngm(
  state_name = "NSW",
  age_breaks = c(seq(0, 85, by = 5), Inf),
  R_target = 1.5
)
ngm_fairfield <- generate_ngm(
  lga_name = "Fairfield (C)",
  age_breaks = c(seq(0, 85, by = 5), Inf),
  R_target = 1.5
)
}