| Title: | Quantification of Population-Level Impact of Vaccination |
| Version: | 0.1.0 |
| Description: | Implements the compartment model from Tokars (2018) <doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of population-wide impact of vaccination against vaccine-preventable diseases such as influenza. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 3.6.0) |
| RoxygenNote: | 7.0.2 |
| LinkingTo: | Rcpp |
| Imports: | Rcpp, tibble, dplyr, rlang, glue, lubridate, magrittr |
| Suggests: | testthat (≥ 2.1.0) |
| NeedsCompilation: | yes |
| Packaged: | 2020-01-10 20:14:47 UTC; khvorov45 |
| Author: | Arseniy Khvorov [aut, cre] |
| Maintainer: | Arseniy Khvorov <khvorov45@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-01-14 11:00:02 UTC |
Generate normal counts
Description
Generates counts from a normal distribution density function.
Usage
generate_counts(init_pop_size, n_timepoints, overall_prop, mean, sd)
Arguments
init_pop_size |
Initial population size |
n_timepoints |
Number of timepoints |
overall_prop |
Overall proportion of the population to be included in the counts over all the timepoints |
mean |
Mean of the normal distribution |
sd |
Standard deviation of the normal distribution |
Value
An integer vector of counts of length n_timepoints
Examples
# Tokars (2018) vaccinations
vacs_tok <- generate_counts(1e6, 304, 0.55, 100, 50)
# Tokars (2018) cases
casen_tok <- generate_counts(1e6, 304, 0.12, 190, 35)
Generate dates
Description
Generate dates given timepoint indices, start date and step unit
Usage
generate_dates(timepoints, start, unit)
Arguments
timepoints |
Integer vector timepoint indices |
start |
Date of index 1 |
unit |
"year" "month" or "day" |
Value
A vector of dates the same length as timepoints
Examples
# Dates from Tokars (2018)
timepoints <- 1L:304L
dates <- generate_dates(timepoints, lubridate::ymd("2017-08-01"), "day")
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)
Simulate an ideal population
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)