Automated and Early Detection of Disease Outbreaks

This function retrieves outbreak signals using the aeddo algorithm. It processes surveillance data, aggregates it based on specified date variables, and applies the aeddo algorithm to detect potential outbreaks.

Usage

get_signals_aeddo(
  data_aggregated,
  number_of_weeks = 52,
  population_size = 1,
  sig_level = 0.95,
  exclude_past_outbreaks = TRUE,
  k = 52 * 3,
  init_theta = c(rep(0, 4), 1),
  lower = c(-1, -0.01, -0.8, -0.8, -6),
  upper = c(100, 0.5, 1, 1, 10),
  method = "L-BFGS-B"
)

Arguments

data_aggregated

data.frame, aggregated data with case counts

number_of_weeks

integer, specifying the number of weeks to generate signals for

population_size

The population size for the aeddo algorithm. Default is 1.

sig_level

The quantile from the random effects distribution used for defining the for outbreak detection threshold, a numeric value between 0 and 1.

exclude_past_outbreaks

logical value indicating whether past outbreak related observations should be excluded from future parameter estimation.

k

An integer specifying the rolling window size employed for parameter estimation.

init_theta

Initial values for model parameters in optimization.

lower

Lower bounds for optimization parameters.

upper

Upper bounds for optimization parameters.

method

The optimization method to use, either "BFGS" (default) or "L-BFGS-B".

Value

An object containing outbreak signals detected by the aeddo algorithm.

References

For information on the aeddo algorithm, refer to the package documentation.

See also

aeddo for details on the aeddo algorithm.

Examples

if (FALSE) { # \dontrun{
data_aggregated <- input_example %>%
  preprocess_data() %>%
  aggregate_data() %>%
  add_rows_missing_dates()
results <- get_signals_aeddo(data_aggregated)
} # }