This vignette describes how to use vimpact to estiamte vaccine impact for members of the VIMC team. It describes how to use methods which require access to the montagu database and so cannot be used by external users.
Vimpact provides a set of methodologies developed by VIMC Science Team for quantifying vaccination impact in terms of cases averted, deaths averted and DALYs (disability-adjusted life years) averted. It also stores functions that extract frequently used data sets from Montagu and Annex database.
[todo] Add a link to our impact calculation methods
Vimpact is primarily developed for VIMC internal users. It contains various functions for reporting and analytic purposes.
There are a set of frequently used functions that extracts data from VIMC databases.
Determine latest touchstone version
Montagu data is touchstone based. Each touchstone may contain
multiple versions as it is developed. The most recent version of
touchstone can be determined by get_touchstone where
con specifies the database machine, and
touchstone_name indicates the touchstone of interest.
get_touchstone(con, touchstone_name = "201710gavi")This will return 201710gavi-6. 201710gavi
touchstone has 6 versions. And Version 6 has the most recent coverage
scenarios.
Extract demographic data Demographic data is frequently used for analysis. It is therefore useful to have a standardized function extracting demographic data from Montagu.
get_population(con, touchstone_pop = "201710gavi-5", demographic_statistic = "int_pop", gender = "Both",
country_ = NULL, year_ = NULL, age_ = NULL)demographic_statistic is demographic_statistic_type
code; gender can be Female, Male or Both if
specified or all three selections if NULL; country_ is all
VIMC countries if NULL or an array of selected countries; similar for
year_ and age_.
Extract vaccination coverage data
extract_vaccination_history(con, touchstone_cov = "201710gavi", touchstone_pop = NULL,
year_min = 2000, year_max = 2100,
vaccine_to_ignore = c("DTP3", "HepB_BD_home", "none"),
disease_to_extract = NULL,
countries_to_extract = NULL,
gavi_support_levels = c("with", "bestminus"),
scenario_type = "default", external_population_estimates = NULL, full_description = TRUE)prepare impact calculation recipe Impact calculation differs according to vaccination scenarios under investigation. Vimpact compares two distinct scenarios, namely the baseline and the focal. Vaccination impact is evaluated as excessive disease burden of focal scenario compared to the baseline. A impact recipe contains meta information of the two scenarios for each particular disease, touchstone and modelling group.
Firstly, recipe_tempate generates a sample impact
recipe, from which users can develop their own recipes. Current vimpact
allows four views of impact recipes (method0, method1, method2a and
method2b) representing vaccine impact by calendar year, year of birth,
year of vaccination (vaccination activity perspective) and year of
vaccination (birth cohort perspective). The following row of code gives
an example recipe of impact by year of birth.
recipe_template(template_version = "201710", method = "method1")Once impact recipes are complete, vimpact prepares meta data needed
for communicating with VIMC databases via
get_meta_from_recipe function. Each pair of scenarios
(focal and baseline) are indexed for further impact calculation.
get_meta_from_recipe(default_recipe = TRUE, method = "method1", recipe_version = "201710", recipe = NULL, con, disease = "Hib")default_recipe, method and recipe_version combined
determine default VIMC impact recipes commonly used for reporting. If
user specified recipe is used (a recipe developed from recipe_template),
the recipe csv file path should be specified as
recipe = <file_path>``.disease` can help to allow
focused evaluation of vaccine impact. If it is NULL, all diseases will
be considered.
impact by calendar year
meta <- get_meta_from_recipe(default_recipe = TRUE, method = "method0", recipe_version = "201710", recipe = NULL, con, disease = "Hib")
meta_s <- split(meta, meta$index)
dat <- lapply(meta_s, function(meta1) get_raw_impact_details(con = con_test, meta1, burden_outcome = "deaths"))
dat <- do.call(rbind, dat)impact by year of birth
meta <- get_meta_from_recipe(default_recipe = TRUE, method = "method1", recipe_version = "201710", recipe = NULL, con, disease = "Hib")
meta_s <- split(meta, meta$index)
dat <- lapply(meta_s, function(meta1) get_raw_impact_details(con = con_test, meta1, burden_outcome = "deaths"))
dat <- do.call(rbind, dat)impact by year of vaccination: impact ratio stratified by activity type (method 2a)
vaccination_years <- 2000:2018
meta <- get_meta_from_recipe(default_recipe = TRUE, method = "method2a", recipe_version = "201710", recipe = NULL, con, disease = "Hib")
meta_s <- split(meta, meta$index)
dat <- lapply(metas, function(meta1) get_raw_impact_details(con = con_test, meta1, burden_outcome = "deaths"))
dat <- do.call(rbind, dat)
fvps <- extract_vaccination_history(con, touchstone_cov = "201710gavi", touchstone_pop = NULL,
year_min = min(vaccination_years), year_max = max(vaccination_years),
disease_to_extract = "Hib",
countries_to_extract = "AFG")
dat2 <- lapply(metas, function(meta1) impact_by_year_of_vaccination(meta1, raw_impact = dat, fvps = fvps,
vaccination_years = vaccination_years))
dat2 <- do.call(rbind, dat2)
impact by year of vaccination: impact ratio stratified by birth cohort (method 2b)
meta <- get_meta_from_recipe(default_recipe = TRUE, method = "method2b", recipe_version = "201710", recipe = NULL, con, disease = "Hib")
meta_s <- split(meta, meta$index)
dat <- lapply(metas, function(meta1) get_raw_impact_details(con = con_test, meta1, burden_outcome = "deaths"))
dat <- do.call(rbind, dat)
fvps <- extract_vaccination_history(con, touchstone_cov = "201710gavi", touchstone_pop = NULL,
year_min = min(vaccination_years), year_max = max(vaccination_years),
disease_to_extract = "Hib",
countries_to_extract = "AFG")
dat2 <- lapply(metas, function(meta1) impact_by_year_of_vaccination(meta1, raw_impact = dat, fvps = fvps,
vaccination_years = vaccination_years))
dat2 <- do.call(rbind, dat2)