acciddasuite

acciddasuite provides a simple pipeline for infectious diseases forecasts. It validates input data (check_data()), optionally applies nowcasting to adjust for reporting delays (get_ncast()), and generates forecasts (get_fcast()).

Installation

You can install the development version of acciddasuite from GitHub with:

# install.packages("pak")
#pak::pak("ACCIDDA/acciddasuite")

Example

library(acciddasuite)
head(example_data)
#> # A tibble: 6 × 5
#>   as_of      location target            target_end_date observation
#>   <date>     <chr>    <chr>             <date>                <dbl>
#> 1 2024-11-17 NY       wk inc covid hosp 2020-08-08              517
#> 2 2024-11-24 NY       wk inc covid hosp 2020-08-08              517
#> 3 2024-12-01 NY       wk inc covid hosp 2020-08-08              517
#> 4 2024-12-08 NY       wk inc covid hosp 2020-08-08              517
#> 5 2024-12-15 NY       wk inc covid hosp 2020-08-08              517
#> 6 2024-12-22 NY       wk inc covid hosp 2020-08-08              517

fcast <- example_data |>
  check_data() |> 
  get_ncast() |> 
  get_fcast(
    eval_start_date = max(example_data$target_end_date) - 28,
    h = 3 # forecast 3 weeks into the future
  )
#> ℹ Using max_delay = 12 from data
#> ℹ Truncating from max_delay = 12 to 4.
#> Warning: 1 error encountered for ARIMA
#> [1]
#> ℹ Some rows containing NA values may be removed. This is fine if not
#> unexpected.
#> ℹ Some rows containing NA values may be removed. This is fine if not
#> unexpected.
#> Warning: 1 error encountered for ARIMA
#> [1] 
#> 
#> 1 error encountered for ARIMA
#> [1] 
#> 
#> 1 error encountered for ARIMA
#> [1]

fcast$plot

Save to myRespiLens format:

to_respilens(fcast, path = "respilens.json")