# NOT RUN {
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)
# }
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