Estimate vaccine-induced immunity levels from routine immunization data

compute_immunity_sample(): Wrapper that estimates immunity for a single imputation sample from imputation through immunity estimation.
compute_immunity_samples(): Performs the complete process for multiple imputation samples. The number of samples depends on your chosen imputation_mode:
- 'deterministic': Fixed at 3 samples (minimum, mean, and maximum aggregations);
- 'stochastic': User-specified via the n_samples argument;
- 'custom': Specify n_samples if your custom function is stochastic; otherwise, all samples will yield identical results.
summarize_immunity_samples(): Aggregates immunity estimates across multiple samples (output of compute_immunity_samples()) by computing summary statistics including mean, median, quantiles, standard deviation, empirical quantiles, and t-based 95% confidence intervals for the mean.

Usage

compute_immunity_samples(
  ri_data,
  live_births = deprecated(),
  vs_info,
  efficacy,
  neighbors,
  ...,
  n_samples = 1,
  birth_seasonality = NULL,
  sample_pair = NULL,
  imputation_mode = NULL,
  sample_mode = NULL,
  bandwidth = 1,
  zero.rm = TRUE,
  .sampler_args = rlang::list2(),
  shift_mode = "full",
  hed_assumption = NULL,
  max_level = 1,
  dd_assumption = NULL,
  vax_imm_type = NULL,
  rho = 0,
  per_imm_type = TRUE,
  quiet = TRUE,
  seed = TRUE
)

compute_immunity_sample(
  ri_data,
  efficacy,
  neighbors,
  ...,
  doses_ratio = deprecated(),
  vs_info = NULL,
  birth_seasonality = NULL,
  shift_prop = NULL,
  sample_pair = NULL,
  imputation_mode = NULL,
  sample_mode = NULL,
  bandwidth = 1,
  zero.rm = TRUE,
  .sampler_args = rlang::list2(),
  shift_mode = "full",
  hed_assumption = NULL,
  max_level = 1,
  dd_assumption = NULL,
  vax_imm_type = NULL,
  rho = 0,
  per_imm_type = TRUE,
  quiet = TRUE
)

summarize_immunity_samples(
  immunity_samples,
  ...,
  by_admin = NULL,
  by_time = NULL
)

Arguments

ri_data: As defined in impute_missing_doses().
live_births: [data.frame] Number of babies born in a given admin unit in a given year. This argument is deprecated as of version 0.2.0. Please include the live births data in the routine immunization data (ri_data) instead with its column named according to birth argument in config_pviem().
vs_info: [data.frame] The vaccination schedule table preprocessed with preprocess_vs_info() which is structured as prep_dummy_vs_info. dummy_vs_info defines the structure of the raw format before preprocessing.
efficacy: [data.frame] Vaccine doses efficacy estimates table preprocessed with preprocess_efficacy() which is structured as prep_dummy_efficacy. efficacy_default defines the structure of the raw format before preprocessing.
neighbors: As defined in handle_extra_doses().
...: Forces optional arguments to be passed by name and allows for future extensions without breaking existing code. Must be empty.
n_samples: [integer(1)] The number of samples to compute. The default is 1. This argument is used only for imputation_mode = "stochastic" and imputation_mode = "custom". Deterministic mode ignores it and always returns the package's fixed minimum, mean, and maximum samples.
birth_seasonality: As defined in shift_doses().
sample_pair: As defined in impute_missing_doses().
imputation_mode: As defined in impute_missing_doses().
sample_mode: As defined in impute_missing_doses().
bandwidth: As defined in impute_missing_doses().
zero.rm: As defined in impute_missing_doses().
.sampler_args: As defined in impute_missing_doses().
shift_mode: As defined in shift_doses().
hed_assumption: As defined in handle_extra_doses().
max_level: As defined in handle_extra_doses().
dd_assumption: As defined in compute_immunity().
vax_imm_type: As defined in compute_immunity_by_type().
rho: As defined in compute_immunity_by_type().
per_imm_type: [logical(1)] If TRUE (default), computes immunity per immunity type. If FALSE, computes immunity per vaccine.
quiet: As defined in handle_extra_doses().
seed: [numeric(1)] A seed for reproducibility as described in furrr::furrr_options().
doses_ratio: [data.frame] This argument is deprecated as of version 0.2.0 and will be removed in future versions. Ratios are now computed internally within the imputation functions (impute_missing_doses()), so there is no need to compute them separately and pass them as an argument.
shift_prop: As defined in shift_doses().
immunity_samples: [data.frame] Immunity estimates samples output from compute_immunity_samples().
by_admin: [character] Optional character vector of administrative columns to group by when summarizing immunity samples. When provided, it must be a subset of the administrative columns defined in config_pviem(). By default, it groups by all administrative columns defined in config_pviem().
by_time: [character] Optional character vector of time columns to group by when summarizing immunity samples. When provided, it must be a subset of year and month (if applicable) columns defined in config_pviem(). By default, it groups by all time columns defined in config_pviem().

Value

compute_immunity_sample(): [data.table] A single immunity-estimate sample, as returned by compute_immunity_by_type() when per_imm_type = TRUE, or compute_immunity() otherwise.
compute_immunity_samples(): [data.table] Multiple samples of the previous function, each identified by the sample column.
summarize_immunity_samples(): [data.table] Summary statistics grouped by the requested administrative and time columns, serotype, and either type or vaccine. Statistic columns are .mean, .sd, .median, .lower and .upper (the empirical 2.5% and 97.5% quantiles), and .ci95l and .ci95u (t-based 95% confidence limits for the mean). Quantiles describe the sample distribution; the confidence limits describe uncertainty in its mean.

Note

When furrr package is installed and plan(multisession) or friends is set, the compute_immunity_samples() will compute the samples in parallel. This makes the computation faster.

Both by_admin and by_time arguments can be used together to specify the grouping variables for summarizing immunity samples. If neither is provided, the function will summarize by all administrative and time columns defined in config_pviem().

Estimate vaccine-induced immunity levels from routine immunization data

Usage

Arguments

Value

Note

See also