| Title: | Impact Study of Vaccination Campaigns |
| Version: | 0.1.0 |
| Description: | Tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) <doi:10.1016/j.vaccine.2015.02.042> and Machado et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.45.1900268> for influenza vaccination impact. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/Epiconcept-Paris/vaccinationimpact/, https://epiconcept-paris.github.io/vaccinationimpact/ |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Depends: | R (≥ 3.5) |
| LazyData: | true |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-10-29 15:50:47 UTC; Yohann Mansiaux |
| Author: | Yohann Mansiaux [aut, cre], Alexandre Blake [aut], James Humphreys [aut], Baltazar Nunes [aut] |
| Maintainer: | Yohann Mansiaux <y.mansiaux@epiconcept.fr> |
| Repository: | CRAN |
| Date/Publication: | 2025-11-03 10:20:10 UTC |
Compute events averted by increasing the final vaccine coverage
Description
Compute events averted by increasing the final vaccine coverage
Usage
compute_events_avertable_by_increasing_coverage(
number_of_events,
cumulative_coverage,
vaccine_coverage_increase,
vaccine_effectiveness
)
Arguments
number_of_events |
number of events |
cumulative_coverage |
cumulative vaccination coverage |
vaccine_coverage_increase |
percentage increase in final vaccine coverage (between 0 and 1) |
vaccine_effectiveness |
vaccine effectiveness |
Value
a list with the new vaccine coverage ("new_vaccine_coverage") and the estimated number of events averted ("nabe")
Examples
data(coverage_and_incidence_mock_data)
data(ve_mock_data)
coverage <- coverage_and_incidence_mock_data$coverage_data
incidence <- coverage_and_incidence_mock_data$incidence_data
vaccine_effectiveness <- ve_mock_data$ve
nabe <- compute_events_avertable_by_increasing_coverage(
number_of_events = incidence$events,
cumulative_coverage = coverage$cumulative_coverage,
vaccine_coverage_increase = 0.1, # 10% increase in final coverage
vaccine_effectiveness = vaccine_effectiveness
)
plot(nabe$new_vaccine_coverage, type = "l",
xlab = "Time", ylab = "Vaccine coverage with 10% increase")
plot(nabe$nabe, type = "l", xlab = "Time", ylab = "Events averted")
Compute events averted by vaccination
Description
Compute events averted by vaccination
Usage
compute_events_averted_by_vaccination(
number_of_events,
cumulative_coverage,
vaccine_effectiveness
)
Arguments
number_of_events |
number of events |
cumulative_coverage |
cumulative vaccination coverage |
vaccine_effectiveness |
vaccine effectiveness |
Details
The number of events averted by vaccination is calculated as described by Machado et al. (2019) doi:10.2807/1560-7917.ES.2019.24.45.1900268.
Value
estimated number of events averted
Examples
data(coverage_and_incidence_mock_data)
data(ve_mock_data)
coverage <- coverage_and_incidence_mock_data$coverage_data
incidence <- coverage_and_incidence_mock_data$incidence_data
vaccine_effectiveness <- ve_mock_data$ve
nae <- compute_events_averted_by_vaccination(
number_of_events = incidence$events,
cumulative_coverage = coverage$cumulative_coverage,
vaccine_effectiveness = vaccine_effectiveness
)
plot(nae, type = "l", xlab = "Time", ylab = "Events averted")
Compute the number of individuals needed to vaccinate to prevent one event according to Machado et al. method
Description
Compute the number of individuals needed to vaccinate to prevent one event according to Machado et al. method
Usage
compute_number_needed_to_vaccinate_machado(
number_of_events,
number_of_events_averted,
population_size,
vaccine_effectiveness
)
Arguments
number_of_events |
number of events |
number_of_events_averted |
number of events averted |
population_size |
population size |
vaccine_effectiveness |
vaccine effectiveness |
Details
The number of individuals needed to vaccinate to prevent one event is calculated as described by Machado et al. (2019) doi:10.2807/1560-7917.ES.2019.24.45.1900268.
Value
The number of individuals needed to vaccinate to avert one event
Examples
data(coverage_and_incidence_mock_data)
data(ve_mock_data)
coverage <- coverage_and_incidence_mock_data$coverage_data
incidence <- coverage_and_incidence_mock_data$incidence_data
vaccine_effectiveness <- ve_mock_data$ve
nae <- compute_events_averted_by_vaccination(
number_of_events = incidence$events,
cumulative_coverage = coverage$cumulative_coverage,
vaccine_effectiveness = vaccine_effectiveness
)
nnv_machado <- compute_number_needed_to_vaccinate_machado(
number_of_events = incidence$events,
number_of_events_averted = nae,
population_size = 1234,
vaccine_effectiveness = vaccine_effectiveness
)
nnv_machado
Compute the number of individuals needed to vaccinate to prevent one event according to Tuite and Fisman method
Description
Compute the number of individuals needed to vaccinate to prevent one event according to Tuite and Fisman method
Usage
compute_number_needed_to_vaccinate_tuite_fisman(
number_of_vaccinated,
number_of_events_averted
)
Arguments
number_of_vaccinated |
number of vaccinated individuals |
number_of_events_averted |
number of events averted |
Details
The number of individuals needed to vaccinate to prevent one event is calculated as described by Tuite and Fisman (2013) doi:10.1016/j.vaccine.2012.11.097.
Value
The number of individuals needed to vaccinate to avert one event
Examples
data(coverage_and_incidence_mock_data)
data(ve_mock_data)
coverage <- coverage_and_incidence_mock_data$coverage_data
incidence <- coverage_and_incidence_mock_data$incidence_data
vaccine_effectiveness <- ve_mock_data$ve
nae <- compute_events_averted_by_vaccination(
number_of_events = incidence$events,
cumulative_coverage = coverage$cumulative_coverage,
vaccine_effectiveness = vaccine_effectiveness
)
nnv_tuite_fisman <- compute_number_needed_to_vaccinate_tuite_fisman(
number_of_vaccinated = coverage$number_of_vaccinated,
number_of_events_averted = nae
)
nnv_tuite_fisman
coverage_and_incidence_mock_data
Description
Coverage and incidence mock data. Coverage values are computed considering a sample size of 1234 individuals.
Usage
coverage_and_incidence_mock_data
Format
A list with two data frames:
- incidence_data
data.frame with weekly incidence data
- coverage_data
data.frame with weekly coverage data
Source
Simulated coverage and incidence data
ve_mock_data
Description
Vaccine effectiveness data.
Usage
ve_mock_data
Format
A data frame with 52 rows and 2 variables:
- week
Date
- ve
numeric: weekly vaccine effectiveness
Source
Simulated vaccine effectiveness data