Bayesian generative modeling for heterogeneous wastewater data applied to COVID-19 forecasting

This repository contains the code to generate the results of evaluating retrospectively the forecast performance of a wastewater-informed forecasting model, both compared to a model without wastewater data and compared to other models submitted to the COVID-19 Forecast Hub over the 2023-24 epidemic season. The model is run using the wwinference R package. Please see that package's GitHub repository for a mathematical description of the model and details on how to install the package and run the model.

This codebase was previously used for real-time submissioned to the COVID-19 Forecast Hub, spanning the dates from February 5th, 2024 to April 29th, 2024. The model used to generate those submissions has since been ported over to the wwinference R package and has been modified from its original structure.

This README is organized into the following sections:

• Project structure describing the contents of this repository
• A description of our evaluation pipeline, used to evaluate the real-time and retrospective forecasts both with and without wastewater and compared to other Hub models
• Our workflow for producing weekly forecasts
• Details on model input data
• Standard CDCGov open source repository information, notices, and disclaimers

Project structure

Folder or file	Purpose
pipeline	R scripts used to fit and post-process each model run in the Azure batch workflow
_targets_eval_postprocessing.R	The targets pipeline used to generate the figures and results in this work, from the post-processed outputs that are generated from running the fitting and postprocessing pipeline on Azure Batch
wweval	Code used for the evaluation pipeline, including pre and post-processing and figure generation
input	Raw input data used to fit the model, including a parameter file and delay pmfs used by the model
output/forecasts	Records of the real-time forecasts and associated metadata submitted to the COVID-19 Forecast Hub from February 5th through April 29th, 2024
output/benchmarking	Metrics of performance from different versions of the model, used to assess model changes
src	Code used to generate the configuration files used in the Azure batch pipeline and the post-processing pipeline
.github	GitHub actions used to set up the CI for the wweval functions
docs	Record of the evaluation plan, note this has not been updated to reflect the current state of the evaluation workflow
scratch	Various scratch files used throughout the project
_targets.R	Now deprecated real-time targets pipeline
model_diagnostics	R markdown presenting summaries of the model diagnostics for use in the real-time production setting

Evaluation pipeline

Retrospective forecasts with and without wastewater data were generated and evaluated for all 22 forecast dates from October 16, 2023 to March 11, 2024, using the wwinference package run in Azure batch. The azure batch pipeline was broken into a fit and post_process job. The outputs from the post_process job were copied onto a local machine to facilitate downstream analysis. This included the scores (generated from scoringutils 1.2.2) from the 2,000 posterior draws of hospital admissions forecasts, the quantiled hospital admissions and wastewater concentraitons from the calibration, nowcast, and forecast period and the input hospital admissions and wastewater data used to generate them.

The _targets_eval_postprocessing.R file provides the pipeline to produce the results for the real-time and retrospective evaluation with and without wastewater and compared to other Hub models.

A note on reproducibility

Unfortunately, the retrospective forecasts are not fully reproducible because they rely on NWSS data, which is not publicly available.

Additionally, because we originally used this single code base for our modeling and production-level pipelining, and have since moved to a separate modeling package (wwinference), we can no longer easily reproduce the model outputs that would have been generated in real-time, as the versions of the model at that time are not in tagged version histories of wwinference. The real-time code we used is available in our commit history.

Deprecated real-time workflow for Covid-19 Forecast Hub submissions

This process was used to produce the real-time forecasts from February 5th through April 29th, 2024. It is no longer being run in production, but we have maintained the text as a record of our process

To produce our submissions to the Covid-19 Forecast Hub, we run a forecasting pipeline every Saturday evening at 9:10 pm EST. In addition to pulling the latest data and using it to fit our inference models, the pipeline generates summary figures, produces a diagnostic report of Markov Chain Monte Carlo convergence diagnostics, and performs data quality checks on the wastewater data. We examine these outputs manually to check for data or model convergence issues.

We produce forecasts of COVID-19 hospital admissions for the 50 states, Puerto Rico, District of Columbia (DC), and the United States. Most forecasts use both wastewater data and hospital admission data, but if a location does not have any wastewater data, the wastewater input data for the model are deemed unreliable, or the model fails to converge, we use the hospital admissions-only model instead. If that model is also unreliable, we do not submit a forecast for that location. In all cases, we record our choice and the reason for it in a run-specific metadata.yaml file, as follows:

• "States without wastewater data": No wastewater data from the past 90 days were available for these locations, so we necessarily used the hospital admissions-only model for them.
• "States we chose to use hospital admissions only model on": We detected anomalies in reported wastewater values for these locations, or the wastewater model fits for these locations did not pass checks for reliability, so we chose to use the hospital admissions-only model for them.
• "States with insufficient wastewater data": We used the wastewater-informed model for these locations, but the actual wastewater data available for them was likely too sparse to meaningfully inform the forecast.
• "States not forecasted": Both the wastewater-informed and the hospital admissions-only models had issues for these locations, so we did not submit forecasts for them.

Individual archived forecasts and their corresponding metadata.yaml files can be found in datestamped subdirectories of the output/forecasts directory, e.g. output/forecasts/2024-02-05.

Model input data

We store all data and configuration for the model in the input folder. This repository does not contain the input h

Hospital admissions data data

For real-time production, we pulled hospital admissions data from the NHSN HealthData.gov public dataset and stored a snapshot of that data (a "vintaged dataset") locally each week. Vintaged data is not stored in this repository, but the pipeline expects this data to live in: input_data/hosp_data/monday_wednesday_datasets

Wastewater data

We used the NWSS API on the DCIPHER platform (non-public data, requires permission from NWSS to access) to obtain wastewater data at the facility level each week, storing a vintaged dataset each week. Vintaged data is not stored in this repository, but the pipeline expects this data to live in: input_data/ww_data/monday_datasets

Contact information

We want feedback and questions! Feel free to submit an issue here on Github, or contact us via this form.

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