acciddasuite

Overview

acciddasuite builds infectious disease forecasts in three steps:

1. get_data() or check_data(): fetch or validate surveillance data.
2. get_ncast() (optional): correct recent weeks for reporting delays.
3. get_fcast(): evaluate models via cross-validation and forecast into the future.

The package relies on the fable modeling framework and follows the standard forecasting workflow described by Hyndman & Athanasopoulos (2021). The overall goal is to provide public health professionals with an easily-adoptable approach to generating an ensemble of outputs from statistical models, evaluating forecasts, and visualizing outputs.

Forecast Planning

To get more information about how to know whether forecasting is the best approach for your task, follow the steps in this article.

Step 1: Get data

We fetch weekly COVID-19 hospital admissions for New York from the CDC NHSN via epidatr.

Setting revisions = TRUE retrieves the full revision history (i.e. all past versions of the data), which is needed for nowcasting.

get_data() returns a validated accidda_data object:

library(acciddasuite)
df <- get_data(pathogen = "covid", geo_value = "ny", revisions = TRUE)
df
#> <accidda_data>
#> 
#> Location: NY 
#> Target:   wk inc covid hosp 
#> Window:   2020-08-08 to 2026-03-28 ( 295 dates )
#> History:  TRUE ( 2024-11-17 to 2026-03-29 )

You can also bring your own data. Just pass it through check_data(). See vignette("external_data") for formatting details.

Step 2: Nowcasting (optional)

The most recent weeks of surveillance data are almost always too low because hospitals are still filing late reports (right truncated). If you feed these raw counts into a forecaster, predictions will be biased downward.

get_ncast() estimates what the recent counts will look like once all reports arrive. With the default max_delay = 4, the last 4 weeks are corrected; everything before that is left untouched.

ncast <- get_ncast(df)
ncast
#> <accidda_ncast>
#> 
#> Nowcasted 4 weeks: 2026-03-07 to 2026-03-28 
#> 
#> $data  corrected series (295 rows)
#> $plot  nowcast visualisation

ncast$plot

The corrected ncast$data contains two extra columns: ncast_lower and ncast_upper (95% CrI) for the corrected weeks. get_fcast() detects these automatically and uses them to propagate nowcasting uncertainty into the final forecast.

Step 3: Forecasting

get_fcast() does two things:

1. Model selection: time series cross-validation on the full (median corrected) series, starting from eval_start_date. Models are ranked by WIS; the best top_n form an equal weight ensemble.
2. Final forecast: projects h weeks into the future. When nowcast columns are present, the forecast is produced from three baselines (lower, median, and upper nowcast estimates) and pooled, so prediction intervals reflect both model uncertainty and nowcast uncertainty.

We set eval_start_date to mark the start of the evaluation window. At least 52 weeks of data must precede this date.

eval_start_date <- max(ncast$data$target_end_date) - 28

Default models are:

• SNAIVE (Seasonal Naïve): Assumes this week will look like the same week last year. The simplest possible baseline.

• ETS (Exponential Smoothing): A weighted average where recent weeks matter more than older ones. Adapts to trends and seasonal patterns.

• THETA: Splits the data into a long-term trend and short-term fluctuations, forecasts each separately, then combines them.

• ARIMA: Learns repeating patterns from past values to predict future ones. Auto-configured to find the best fit.

We use pipetime::time_pipe() to log how long the model selection and forecasting steps take.

fcast <- get_fcast(
  ncast,
  eval_start_date = eval_start_date,
  top_n = 4,
  h = 4
) |> 
  pipetime::time_pipe("base fcast", log = "log")

fcast
#> <accidda_fcast>
#> 
#> Models evaluated:
#>  model_id       wis
#>    <char>     <num>
#>     THETA  39.34596
#>       ETS  77.55883
#>  ENSEMBLE  94.74694
#>    SNAIVE 245.41487
#> 
#> Forecast horizon:
#>   From: 2026-02-28 
#>   To:   2026-04-25 
#> 
#> Contents:
#>   $hubcast   hub forecast object
#>   $score     model ranking table
#>   $plot      ggplot2 object

fcast$plot

View forecast evaluation by viewing the score element of the object:

fcast$score
#> Key: <model_id>
#>    model_id       wis interval_coverage_50 interval_coverage_95
#>      <char>     <num>                <num>                <num>
#> 1:    THETA  39.34596                 1.00                    1
#> 2:      ETS  77.55883                 0.25                    1
#> 3: ENSEMBLE  94.74694                 0.25                    1
#> 4:   SNAIVE 245.41487                 0.00                    1
#>    wis_relative_skill
#>                 <num>
#> 1:          0.4286973
#> 2:          0.8450490
#> 3:          1.0323235
#> 4:          2.6739391

Adding custom models

Any model compatible with the fable framework can be passed via extra_models:

library(fable)
library(fable.prophet)
extra <- list(
  CUSTOM_ARIMA = ARIMA(observation ~ pdq(1,1,0)),
  PROPHET = prophet(observation ~ season("year")),
  EPIESTIM = EPIESTIM(observation, mean_si = 3, std_si = 2, rt_window = 7)
)

fcast <- get_fcast(
  ncast,
  eval_start_date = eval_start_date,
  top_n = 4,
  h = 3,
  extra_models = extra
) |> 
  pipetime::time_pipe("extra fcast", log = "log")

You can check how long each step took by calling pipetime::get_log():

pipetime::get_log()
#> $log
#>             timestamp       label duration unit
#> 1 2026-04-07 18:45:06  base fcast 18.03962 secs
#> 2 2026-04-07 18:45:28 extra fcast 23.81729 secs

Submit to RespiLens

RespiLens is a platform for sharing respiratory disease forecasts. Use to_respilens() to export the forecast as JSON for upload to MyRespiLens.

to_respilens(fcast, "respilens.json")