Generate Viral Respiratory Forecast Report — generate_forecast

Generates a full-season forecast report for viral respiratory diseases as an HTML document.

Usage

generate_forecast_report(
  input_data_dir = NULL,
  output_dir = NULL,
  n_days = 7,
  validate_window_size = 7,
  smooth = FALSE,
  disease_season = NULL,
  seed = 123
)

Arguments

input_data_dir

Path to input CSV data. Must contain columns: date, confirm, disease_type. Allowed values for disease_type: "flu_a", "flu_b", "rsv", "sars_cov2", "custom".

output_dir

Path to output directory for the rendered HTML report.

n_days

Number of days ahead to forecast. Default is 7.

validate_window_size

The number of days between each validation window. Default is 7.

smooth

Logical indicating whether smoothing should be applied in the forecast. Default is TRUE.

disease_season

An optional named list specifying the seasonal date ranges for each disease. Each element should be either:

NULL (indicating no defined season), or
a two-date vector in "YY-MM-DD" format (e.g., c("2024-09-01", "2025-03-01")) defining the start and end of the season for that disease.

For example: disease_season = list( flu_a = c("2024-09-01", "2025-03-01"), rsv = c("2024-09-01", "2025-03-01"), sars_cov2 = NULL )

This will produce a report where influenza A and RSV seasons run from September 1, 2024 to March 1, 2025, while no season is defined for SARS-CoV-2.

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.

Value

Invisibly returns the path to the rendered HTML report.

Examples

# \donttest{
data <- simulate_data(start_date = "2024-01-07", #starting Sunday
)
diseases <- c("flu_a", "rsv", "sars_cov2")
data$date <- lubridate::ymd(data$date)
vri_data_list <- purrr::set_names( purrr::map2( rep(list(data), length(diseases)),
                                  diseases,
                                  ~ get_aggregated_data(.x, "date", .y)
                                 ),
                            diseases
)
# Save the simulated data
df <- purrr::imap_dfr(
vri_data_list,
\(df, disease) dplyr::mutate(df, disease_type = disease)
)
tmp_dir <- tempdir() # temporary directory for example for saving data
data_path <- file.path(tmp_dir, "simulated_data.csv")
write.csv(df, data_path, row.names = FALSE)

output_path <- tempdir() # output directory for report (temporary as example)
generate_forecast_report(input_data_dir = data_path,
                         output_dir = output_path,
                         n_days = 7,
                         validate_window_size = 7,
                         smooth = FALSE)
#> [1] "/tmp/Rtmp3ztFE7/vriforecasting_report.html"
# }