ViroReportR (version 1.0.4)

generate_forecast_report: Generate Viral Respiratory Forecast Report

Description

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
)

Value

Invisibly returns the path to the rendered HTML report.

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.

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)
# }