Package 'vaccinationimpact'

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) https://doi.org/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) https://doi.org/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) https://doi.org/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

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