Apply vaccination effects to next generation contact matrices

Applies the effect of vaccination on the next generation of infections, to understand and describe the reduction of acquisition and transmission in each age group.

Usage

apply_vaccination(ngm, data, coverage_col, acquisition_col, transmission_col)

Arguments

ngm: next generation matrices. See generate_ngm() for creating next generation matrices of a state or a local government area for specific age groups
data: data frame with location specific information on vaccine coverage, efficacy of acquisition/susceptibility and efficacy of transmission/infectiousness for the ordered age groups from lowest to highest of the next generation matrix
coverage_col: bare variable name for the column with information on vaccine coverage by age groups
acquisition_col: bare variable name for the column with information on efficacy of acquisition
transmission_col: bare variable name for the column with information on efficacy of transmission

Value

list of contact matrices, one for each setting with reduction in transmission matching the next generation matrices

Details

Vaccination improves a person's immunity from a disease. When a sizeable section of the population receives vaccinations or when vaccine coverage is sufficient enough, the likelihood that the unvaccinated population will contract the disease is decreased. This helps to slow infectious disease spread as well as lessen its severity. For this reason, it is important to understand how much of a reduction in probability of acquisition (the likelihood that an individual will contract the disease), and probability of transmission (the likelihood that an individual will spread the disease after contracting it), has occurred as an the effect of vaccination, in other words the effect of vaccination on the next generation of infections.

apply_vaccination returns the percentage reduction in acquisition and transmission in each age group. It does this by taking the outer product of these reductions in acquisition and transmission by age group, creating a transmission reduction matrix. The next generation matrices with the vaccination effects applied are then produced using the obtained transmission reduction matrix and the next generation matrices passed to the function as an argument.

Examples

# examples take 20 second to run so skipping
if (FALSE) {
# example data frame with vaccine coverage, acquisition and transmission
# efficacy of different age groups
vaccination_effect_example_data

# Generate next generation matrices

perth <- abs_age_lga("Perth (C)")
perth_hh <- get_abs_per_capita_household_size(lga = "Perth (C)")

age_breaks_0_80 <- c(seq(0, 80, by = 5), Inf)

# refit the model - note that the default if age_breaks isn't specified is
# 0 to 75
perth_contact_0_80 <- extrapolate_polymod(
  perth,
  per_capita_household_size = perth_hh,
  age_breaks = age_breaks_0_80
)

perth_ngm_0_80 <- generate_ngm(perth_contact_0_80,
  age_breaks = age_breaks_0_80,
  per_capita_household_size = perth_hh,
  R_target = 1.5
)

# In the old way we used to be able to pass age_breaks_0_80 along
generate_ngm_oz(
  lga_name = "Perth (C)",
  age_breaks = age_breaks_0_80,
  R_target = 1.5
)


# another way to do this using the previous method for generating NGMs
# The number of age breaks must match the vaccination effect data
ngm_nsw <- generate_ngm_oz(
  state_name = "NSW",
  age_breaks = c(seq(0, 80, by = 5), Inf),
  R_target = 1.5
)

# Apply vaccination effect to next generation matrices
ngm_nsw_vacc <- apply_vaccination(
  ngm = ngm_nsw,
  data = vaccination_effect_example_data,
  coverage_col = coverage,
  acquisition_col = acquisition,
  transmission_col = transmission
)
}