method1 {impactflu}

Analysis methods from Tokars (2018)

Description

Method 1 was said to be as current. Method 3 was determined to be the least biased.

Usage

method1(init_pop_size, vaccinations, cases, ve)

method3(init_pop_size, vaccinations, cases, ve)

Arguments

init_pop_size

Integer initial population size

vaccinations

Integer vector counts of vaccinations

cases

Integer vector counts of cases

ve

Vector vaccine effectiveness. If length 1, assumed to not vary with time.

Value

A tibble with the following columns (method-dependent):

cases

Observed cases

vaccinations

Observed vaccinations

ve

Assumed vaccine effectiveness

pvac

Proportion of the starting population vaccinated

vc_lag

Vaccine coverage lagged

pops

Susceptible population

pflu

Infection risk

popn

Non-cases is absence of vaccination

cases_novac

Cases in absence of vaccination

avert

Expected number of vaccinations

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

library(dplyr)

# Simulate a population
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
)

# Summarise by month
pop_tok_month <- pop_tok %>%
  mutate(
    datestamp = generate_dates(
      timepoint, lubridate::ymd("2017-08-01"), "day"
    ),
    year = lubridate::year(datestamp),
    month = lubridate::month(datestamp)
 ) %>%
 group_by(year, month) %>%
 summarise(
   vaccinations = sum(vaccinations), cases = sum(cases), ve = mean(ve)
 ) %>%
 ungroup()

# Estimate averted cases using the two different methods
m1 <- method1(
  nsam, pop_tok_month$vaccinations, pop_tok_month$cases, pop_tok_month$ve
)
m3 <- method3(
  nsam, pop_tok_month$vaccinations, pop_tok_month$cases, pop_tok_month$ve
)
sum(m1$avert)
sum(m3$avert)