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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,
  ...
)

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.

...

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
)