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Forecast daily epidemic cases using EpiEstim

Source: R/fit_epiestim_model_functions.R
generate_forecast.Rd

This function prepares epidemic data, estimates the reproduction number (\(R_t\)) using fit_epiestim_model, and produces short-term forecasts of daily confirmed cases with project_epiestim_model.

It removes early periods with no cases, checks data validity, optionally smooths the epidemic curve, and then generates forward projections of cases for a specified number of days.

Usage

generate_forecast(
  data,
  start_date,
  window_size = 7,
  n_days = 7,
  type = NULL,
  smooth_data = FALSE,
  smoothing_cutoff = 10,
  seed = 123,
  ...
)

Arguments

data

data frame Must contain two columns:

  • date: observation dates

  • confirm: daily confirmed cases

start_date

Date Date after which the epidemic is considered to have started. Data before this date is removed.

window_size

Integer Length of the sliding window (in days) used for reproduction number estimation. Default is 7.

n_days

Integer Number of future days to forecast. Default is 7.

type

character Type of epidemic. Must be one of "flu_a", "flu_b", "rsv", "sars_cov2", or "custom". Passed to fit_epiestim_model.

smooth_data

logical Whether to smooth the input daily case counts before estimation. Default is FALSE.

smoothing_cutoff

Integer Cutoff parameter for smoothing. Only used if smooth_data = TRUE. Default is 10.

seed

Integer or NULL Random seed used to ensure reproducibility of the forecast. If provided, the same input data will produce identical results across runs. If set to NULL, results may vary between runs due to stochastic sampling in reproduction number estimation and projection steps. Default is 123.

...

Additional arguments passed to fit_epiestim_model.

Value

A data frame of forecasted daily incidence with columns:

  • date: date of forecast

  • p50, p25, p75, p025, p975: forecast quantiles

  • min_sim, max_sim: forecast range

Details

  • Data prior to the first non-zero confirm value is excluded.

  • Input is checked for validity (sufficient days, proper format).

  • If smoothing is enabled, case counts are adjusted before fitting.

  • Forecasts are generated from the fitted EpiEstim model and returned with quantiles (2.5%, 25%, 50%, 75%, 97.5%), minimum, and maximum.

See also

fit_epiestim_model for reproduction number estimation, project_epiestim_model for forward simulations.

Examples


# Create sample test rsv data
disease_type <- "rsv"
test_data <- simulate_data()
formatted_data <- get_aggregated_data(
  test_data,
  number_column = disease_type,
  date_column = "date",
  start_date = "2024-04-01",
  end_date = "2024-05-01"
)

# Run a 7 day forecast with smoothing
res_smooth <- generate_forecast(
  data = formatted_data,
  start_date = "2024-04-01",
  n_days = 7,
  type = "rsv",
  smooth_data = FALSE
)