Note: This package is currently in development and may not work as expected. Please file bug reports as issues in this repo, and we will do our best to address them quickly.
Installation
To install (unless you’re making changes to the package, use the stable version):
# Stable version
pak::pkg_install("cmu-delphi/epipredict@main")
# Dev version
pak::pkg_install("cmu-delphi/epipredict@dev")Documentation
You can view documentation for the main branch at https://cmu-delphi.github.io/epipredict.
Goals for epipredict
We hope to provide:
- A set of basic, easy-to-use forecasters that work out of the box. You should be able to do a reasonably limited amount of customization on them. For the basic forecasters, we currently provide:
- Baseline flatline forecaster
- Autoregressive forecaster
- Autoregressive classifier
- CDC FluSight flatline forecaster
- A framework for creating custom forecasters out of modular components. There are four types of components:
- Preprocessor: do things to the data before model training
- Trainer: train a model on data, resulting in a fitted model object
- Predictor: make predictions, using a fitted model object
- Postprocessor: do things to the predictions before returning
Target audiences:
- Basic. Has data, calls forecaster with default arguments.
- Intermediate. Wants to examine changes to the arguments, take advantage of built in flexibility.
- Advanced. Wants to write their own forecasters. Maybe willing to build up from some components.
The Advanced user should find their task to be relatively easy. Examples of these tasks are illustrated in the vignettes and articles.
See also the (in progress) Forecasting Book.
Intermediate example
The package comes with some built-in historical data for illustration, but up-to-date versions of this could be downloaded with the {epidatr} package and processed using {epiprocess}.1
library(epipredict)
case_death_rate_subset
#> An `epi_df` object, 20,496 x 4 with metadata:
#> * geo_type = state
#> * time_type = day
#> * as_of = 2022-05-31 12:08:25.791826
#>
#> # A tibble: 20,496 × 4
#> geo_value time_value case_rate death_rate
#> * <chr> <date> <dbl> <dbl>
#> 1 ak 2020-12-31 35.9 0.158
#> 2 al 2020-12-31 65.1 0.438
#> 3 ar 2020-12-31 66.0 1.27
#> 4 as 2020-12-31 0 0
#> 5 az 2020-12-31 76.8 1.10
#> 6 ca 2020-12-31 96.0 0.751
#> 7 co 2020-12-31 35.8 0.649
#> 8 ct 2020-12-31 52.1 0.819
#> 9 dc 2020-12-31 31.0 0.601
#> 10 de 2020-12-31 65.2 0.807
#> # ℹ 20,486 more rowsTo create and train a simple auto-regressive forecaster to predict the death rate two weeks into the future using past (lagged) deaths and cases, we could use the following function.
two_week_ahead <- arx_forecaster(
case_death_rate_subset,
outcome = "death_rate",
predictors = c("case_rate", "death_rate"),
args_list = arx_args_list(
lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
ahead = 14
)
)
two_week_ahead
#> ══ A basic forecaster of type ARX Forecaster ═══════════════════════════════
#>
#> This forecaster was fit on 2024-11-11 11:38:31.
#>
#> Training data was an <epi_df> with:
#> • Geography: state,
#> • Time type: day,
#> • Using data up-to-date as of: 2022-05-31 12:08:25.
#>
#> ── Predictions ─────────────────────────────────────────────────────────────
#>
#> A total of 56 predictions are available for
#> • 56 unique geographic regions,
#> • At forecast date: 2021-12-31,
#> • For target date: 2022-01-14.
#> In this case, we have used a number of different lags for the case rate, while only using 3 weekly lags for the death rate (as predictors). The result is both a fitted model object which could be used any time in the future to create different forecasts, as well as a set of predicted values (and prediction intervals) for each location 14 days after the last available time value in the data.
two_week_ahead$epi_workflow
#>
#> ══ Epi Workflow [trained] ══════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> Postprocessor: Frosting
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────
#>
#> 6 Recipe steps.
#> 1. step_epi_lag()
#> 2. step_epi_lag()
#> 3. step_epi_ahead()
#> 4. step_naomit()
#> 5. step_naomit()
#> 6. step_training_window()
#>
#> ── Model ───────────────────────────────────────────────────────────────────
#>
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#>
#> Coefficients:
#> (Intercept) lag_0_case_rate lag_1_case_rate lag_2_case_rate
#> -0.0073358 0.0030365 0.0012467 0.0009536
#> lag_3_case_rate lag_7_case_rate lag_14_case_rate lag_0_death_rate
#> 0.0011425 0.0012481 0.0003041 0.1351769
#> lag_7_death_rate lag_14_death_rate
#> 0.1471127 0.1062473
#>
#> ── Postprocessor ───────────────────────────────────────────────────────────
#>
#> 5 Frosting layers.
#> 1. layer_predict()
#> 2. layer_residual_quantiles()
#> 3. layer_add_forecast_date()
#> 4. layer_add_target_date()
#> 5. layer_threshold()
#> The fitted model here involved preprocessing the data to appropriately generate lagged predictors, estimating a linear model with stats::lm() and then postprocessing the results to be meaningful for epidemiological tasks. We can also examine the predictions.
two_week_ahead$predictions
#> # A tibble: 56 × 5
#> geo_value .pred .pred_distn forecast_date target_date
#> <chr> <dbl> <dist> <date> <date>
#> 1 ak 0.449 quantiles(0.45)[2] 2021-12-31 2022-01-14
#> 2 al 0.574 quantiles(0.57)[2] 2021-12-31 2022-01-14
#> 3 ar 0.673 quantiles(0.67)[2] 2021-12-31 2022-01-14
#> 4 as 0 quantiles(0.12)[2] 2021-12-31 2022-01-14
#> 5 az 0.679 quantiles(0.68)[2] 2021-12-31 2022-01-14
#> 6 ca 0.575 quantiles(0.57)[2] 2021-12-31 2022-01-14
#> 7 co 0.862 quantiles(0.86)[2] 2021-12-31 2022-01-14
#> 8 ct 1.07 quantiles(1.07)[2] 2021-12-31 2022-01-14
#> 9 dc 2.12 quantiles(2.12)[2] 2021-12-31 2022-01-14
#> 10 de 1.09 quantiles(1.09)[2] 2021-12-31 2022-01-14
#> # ℹ 46 more rowsThe results above show a distributional forecast produced using data through the end of 2021 for the 14th of January 2022. A prediction for the death rate per 100K inhabitants is available for every state (geo_value) along with a 90% predictive interval.