sim_reference: Simulate an ideal population

In impactflu: Quantification of Population-Level Impact of Vaccination

Description

Simulates an ideal population using the reference model from Tokars (2018).

Usage

sim_reference(
  init_pop_size,
  vaccinations,
  cases_novac,
  ve,
  lag,
  deterministic,
  seed = sample.int(.Machine$integer.max, 1)
)

Arguments

init_pop_size	Integer initial population size
vaccinations	Integer vector number of vaccinations at every timepoint
cases_novac	Integer vector number of cases at every timepoint
ve	Vaccine effectiveness (proportion)
lag	Integer lag period measured in timepoints
deterministic	Boolean whether to make the simulation deterministic
seed	Integer seed to use

Value

A tibble with the following columns:

timepoint	Index of timepoint
vaccinations	Expected number of vaccinations
cases_novac	Expected number of cases in absence of vaccination
ve	Expected vaccine effectiveness
pflu	Flu incidence
cases	Actual number of cases
popn	Non-cases in absence of vaccination
pvac	Proportion of starting population vaccinated
b	Number vaccinated at that time
A	Non-vaccinated non-cases
B	Vaccinated non-cases lagging
E	Non-vaccinated cases

References

Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331–7337. doi:10.1016/j.vaccine.2018.10.026

Examples

# Population from Tokars (2018)
nsam <- 1e6L
ndays <- 304L
pop_tok <- sim_reference(
  init_pop_size = nsam,
  vaccinations = generate_counts(nsam, ndays, 0.55, mean = 100, sd = 50),
  cases_novac = generate_counts(nsam, ndays, 0.12, mean = 190, sd = 35),
  ve = 0.48,
  lag = 14,
  deterministic = TRUE
)
head(pop_tok)
sum(pop_tok$avert)

impactflu documentation built on Jan. 14, 2020, 5:06 p.m.