acciddasuite

Overview

acciddasuite builds infectious disease forecasts in a few steps:

1. get_data() or check_data(): fetch or validate surveillance data.
2. get_ncast() (optional): correct recent weeks for reporting delays.
3. get_cv() (optional): evaluate candidate models by time series cross-validation.
4. get_fcast(): ensemble the best models into a forward-looking forecast.

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, evaluating forecasts, and visualizing infectious disease forecasts.

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 2026-06-06 ( 305 dates )
#> Interval: 7 days
#> History:  TRUE ( 2024-11-17 to 2026-06-07 )

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>
#> 
#> Location: NY 
#> Target:   wk inc covid hosp 
#> Nowcasted 2 weeks: 2026-05-30 to 2026-06-06 
#> 
#> $data  corrected series (305 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

Forecasting is split into two steps:

1. get_cv() — model selection: time series cross-validation on the full (median corrected) series, starting from eval_start_date. Models are ranked by WIS and interval coverage.
2. get_fcast() — final forecast: reuses the ranking to ensemble the best top_n models and 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:

• NAIVE (Naïve / random walk): Carries the last observed value forward. 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.

cv <- get_cv(
  ncast,
  eval_start_date = eval_start_date,
  h = 4
)
cv
#> <accidda_cv>
#> 
#> Models ranked (cross-validation):
#> # A tibble: 3 × 2
#>   model_id   wis
#>   <chr>    <dbl>
#> 1 ETS       29.4
#> 2 THETA     34.1
#> 3 NAIVE     35.1
#> 
#> Evaluated from 2026-05-09 | horizon 4 weeks | NY 
#> 
#> Contents:
#>   $forecasts  per-origin forecasts (model_out_tbl)
#>   $oracle     observed truth (oracle_output)
#>   $score      model ranking table
#>   $models     model specifications
#>   $meta       eval_start_date, h, location, target, interval

fcast <- get_fcast(cv, top_n = 3)
fcast
#> <accidda_fcast>
#> 
#> Models ranked (cross-validation):
#> # A tibble: 3 × 2
#>   model_id   wis
#>   <chr>    <dbl>
#> 1 ETS       29.4
#> 2 THETA     34.1
#> 3 NAIVE     35.1
#> 
#> Forecast horizon:
#>   From: 2026-06-13 
#>   To:   2026-07-04 
#> 
#> Contents:
#>   $hub    hub forecast object (model_out_tbl, oracle_output)
#>   $score  model ranking table, or NULL
#>   $meta   models, top_n, h, location, target, interval, nowcast

Adding custom models

Any model compatible with the fable framework can be passed to get_cv() via models. Compose with default_models() to keep the built-ins alongside your own:

library(fable)
library(fable.prophet)
library(EpiEstim)
library(projections)
my_models <- c(
  default_models(),
  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)
  )
)

cv <- get_cv(
  ncast,
  eval_start_date = eval_start_date,
  h = 3,
  models = my_models
)

fcast <- get_fcast(cv, top_n = 3)

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")