The goal of vaccinationimpact is to assess the impact of vaccination campaigns using the following estimates:
- Number of events averted by vaccination (NAE)
- Number of avertable events considering an increase in final coverage (NAbE)
- Number needed to vaccinate (NNV) to prevent one event
Installation
You can install the development version of vaccinationimpact from GitHub with:
# install.packages("devtools")
devtools::install_github("Epiconcept-Paris/vaccinationimpact")Example
We use some toy data to illustrate the usage of the package: weekly coverage, incidence and vaccine effectiveness are provided in the package.
library(vaccinationimpact)
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$veNAE
nae <- compute_events_averted_by_vaccination(
number_of_events = incidence$events,
cumulative_coverage = coverage$cumulative_coverage,
vaccine_effectiveness = vaccine_effectiveness
)
nae
#> [1] 2.285438 8.405426 14.187702 19.150751 22.333578 26.258535 25.277745
#> [8] 14.479245 14.524775 20.687536 9.940025 5.904505 6.979458 3.795576
#> [15] 4.214308 1.203321 1.917519 3.123184 2.798718 1.489643 0.000000
#> [22] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [29] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [36] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [43] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [50] 0.000000 0.000000 0.000000NAbE
nabe <- compute_events_avertable_by_increasing_coverage(
number_of_events = incidence$events,
cumulative_coverage = coverage$cumulative_coverage,
vaccine_coverage_increase = 0.1, # 10% increase in final coverage
vaccine_effectiveness = vaccine_effectiveness
)
nabe$nabe
#> [1] 2.587606 9.637167 16.574011 22.651941 26.915232 32.599316 31.352360
#> [8] 17.750085 18.067588 25.959763 12.974325 7.574670 8.970289 4.844898
#> [15] 5.299678 1.593502 2.451064 3.811307 3.564364 1.847972 0.000000
#> [22] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [29] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [36] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [43] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [50] 0.000000 0.000000 0.000000NNV
NNV can be estimated using 2 methods: Machado et al. and Tuite and Fisman (see vignette for more details).
Machado et al. method
sample_size <- 1234
nnv_machado <- compute_number_needed_to_vaccinate_machado(
number_of_events = incidence$events,
number_of_events_averted = nae,
population_size = sample_size,
vaccine_effectiveness = vaccine_effectiveness
)
nnv_machado
#> [1] 41.12997 29.50475 26.92473 27.41407 29.01461 27.30541 30.97586
#> [8] 58.08314 60.58614 43.89116 93.86294 160.55538 137.54650 255.03374
#> [15] 230.88015 812.75083 511.59870 314.74289 351.58960 661.23225 NA
#> [22] NA NA NA NA NA NA NA
#> [29] NA NA NA NA NA NA NA
#> [36] NA NA NA NA NA NA NA
#> [43] NA NA NA NA NA NA NA
#> [50] NA NA NATuite and Fisman method
nnv_tuite_fisman <- compute_number_needed_to_vaccinate_tuite_fisman(
number_of_vaccinated = cumsum(coverage$number_of_vaccinated),
number_of_events_averted = nae
)
nnv_tuite_fisman
#> [1] 41.12997 29.50475 26.92473 27.41407 29.01461 27.30541 30.97586
#> [8] 58.08314 60.58614 43.89116 93.86294 160.55538 137.54650 255.03374
#> [15] 230.88015 812.75083 511.59870 314.74289 351.58960 661.23225 NA
#> [22] NA NA NA NA NA NA NA
#> [29] NA NA NA NA NA NA NA
#> [36] NA NA NA NA NA NA NA
#> [43] NA NA NA NA NA NA NA
#> [50] NA NA NAMore information can be found in the vignette.