Get Setting Transmission Matrices — get_setting_transmission

Given some age breaks, return a named list of matrices containing age-specific relative per-contact transmission probability matrices for each of 4 settings: home, school, work, other. These can be combined with contact matrices to produce setting-specific relative next generation matrices (NGMs). These can be scaled to match a required reproduction number based on the dominant eigenvalue of the all-settings NGM (the elementwise sum of all setting NGMs).

Usage

get_setting_transmission_matrices(
  age_breaks = c(seq(0, 80, by = 5), Inf),
  asymptomatic_relative_infectiousness = 0.5,
  susceptibility_estimate = c("davies_updated", "davies_original")
)

Arguments

age_breaks: vector of age breaks, defaults to c(seq(0, 80, by = 5), Inf)
asymptomatic_relative_infectiousness: the assumed ratio of onward infectiousness between asymptomatic and symptomatic cases. This represents the infectiousness of asymptomatic relative to symptomatic. Default value is 0.5, which means the asymptomatic cases are 50% less infectious than symptomatic cases.
susceptibility_estimate: Which estimate to use for susceptibility by age. Either, the smoothed original Davies et al estimates, "davies_original" or, the set updated to match UK under-16 infections (the default), "davies_updated".

Value

list of matrices, containing the relative per-contact transmission probability for each setting

Details

These matrices are created from: an estimate of the clinical fraction for each age (inferred by applying a smoothing spline to the mean estimates from Davies et al.); an assumption of the infectiousness of asymptomatics relative to symptomatics (provided as an argument); estimates of the relative susceptibility to infection of individuals of different ages, inferred from a smoothing-spline estimate of the mean relative susceptibility estimate from Davies et al., combined with a re-estimation of the susceptibility profile for under-16s, estimated in a similar way but to the age-distribution of infections in England from the UK ONS prevalence survey (rather than case counts with may undercount children), assuming the above clinical fraction estimates, and accounting for vaccination, reduced mixing, and reduced transmissibility in work and other settings due to hygiene behaviour; and estimates of the relative transmissibility in household vs non-household settings - scaled linearly for non-household transmission and binomially for household transmission (so that onward infections do not to exceed the number of other household members).

When using this data, ensure that you cite this package, and the original authors of the paper from which these estimates were derived:

Davies, N.G., Klepac, P., Liu, Y. et al. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med 26, 1205–1211 (2020). https://doi.org/10.1038/s41591-020-0962-9

Examples

if (FALSE) {
# fit polymod model
setting_models <- fit_setting_contacts(
  contact_data_list = get_polymod_setting_data(),
  population = get_polymod_population()
)

# define age breaks for prediction
age_breaks <- c(seq(0, 80, by = 5), Inf)

# define a new population age distribution to predict to
fairfield <- abs_age_lga("Fairfield (C)")

# predict setting-specific contact matrices to a new population
contact_matrices <- predict_setting_contacts(
  population = fairfield,
  contact_model = setting_models,
  age_breaks = age_breaks
)

# remove the 'all' matrix, keep the other four settings
contact_matrices <- contact_matrices[c("home", "school", "work", "other")]

# get setting-specific per-contact transmission rate matrices for the same
# age aggregations
transmission_matrices <- get_setting_transmission_matrices(
  age_breaks = age_breaks
)

# combine them to get setting-specific (unscaled) next-generation matrices
next_generation_matrices <- mapply(
  FUN = `*`,
  contact_matrices,
  transmission_matrices,
  SIMPLIFY = FALSE
)

# get the all-settings NGM
ngm_overall <- Reduce("+", next_generation_matrices)
}