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The goal of {vaccinationimpact} is to assess the impact
of vaccination campaigns using the following estimates:
You can install the development version of vaccinationimpact from GitHub with:
# install.packages("devtools")
devtools::install_github("Epiconcept-Paris/vaccinationimpact")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$venae <- compute_events_averted_by_vaccination(
number_of_events = incidence$events,
cumulative_coverage = coverage$cumulative_coverage,
vaccine_effectiveness = vaccine_effectiveness
)
nae
#> [1] 2.285438 8.405426 14.187702 19.150751 22.333578 26.258535 25.277745
#> [8] 14.479245 14.524775 20.687536 9.940025 5.904505 6.979458 3.795576
#> [15] 4.214308 1.203321 1.917519 3.123184 2.798718 1.489643 0.000000
#> [22] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [29] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [36] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [43] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [50] 0.000000 0.000000 0.000000nabe <- compute_events_avertable_by_increasing_coverage(
number_of_events = incidence$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 can be estimated using 2 methods: Machado et al. and Tuite and Fisman (see vignette for more details).
sample_size <- 1234
nnv_machado <- compute_number_needed_to_vaccinate_machado(
number_of_events = incidence$events,
number_of_events_averted = nae,
population_size = sample_size,
vaccine_effectiveness = vaccine_effectiveness
)
nnv_machado
#> [1] 41.12997 29.50475 26.92473 27.41407 29.01461 27.30541 30.97586
#> [8] 58.08314 60.58614 43.89116 93.86294 160.55538 137.54650 255.03374
#> [15] 230.88015 812.75083 511.59870 314.74289 351.58960 661.23225 NA
#> [22] NA NA NA NA NA NA NA
#> [29] NA NA NA NA NA NA NA
#> [36] NA NA NA NA NA NA NA
#> [43] NA NA NA NA NA NA NA
#> [50] NA NA NAnnv_tuite_fisman <- compute_number_needed_to_vaccinate_tuite_fisman(
number_of_vaccinated = cumsum(coverage$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.
This package was developed to conduct an impact study on COVID-19 vaccination as part of the VEBIS project funded by the ECDC.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.