Bayesian Generative Modeling for Heterogeneous Wastewater Data Applied to COVID-19 Forecasting
medRxiv · 2026
Work in progress2026
Abstract
Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissions at the state and territorial levels in the United States, based on incident hospital admissions data and, optionally, SARS-CoV-2 wastewater concentration data from multiple wastewater sampling sites. From February through April 2024, we produced real-time wastewater-informed COVID-19 forecasts using development versions of the model and submitted them to the United States COVID-19 Forecast Hub (“the Hub”). We then published an open-source R package, wwinference , that implements the model with or without wastewater as an input. Using proper scoring rules and measures of model calibration, we assess both our real-time submissions to the Hub and retrospective hypothetical forecasts from wwinference made with and without wastewater data. While the models performed similarly with and without the wastewater signal included, there was substantial heterogeneity for individual locations and dates where wastewater data meaningfully improved or degraded the model’s forecast performance. Compared to other models submitted to the Hub during the period spanned by our submissions, the real-time wastewater-informed version of our model ranked fourth of 10 models, with the hospital admissions-only version of our model ranking second out of 10 models. Across the 2023-2024 winter epidemic wave, retrospective forecasts from wwinference would have performed similarly with and without the wastewater signal included: fifth and fourth out of 10 models, respectively. To better understand the drivers of differential forecast performance with and without wastewater, we performed an exploratory analysis investigating the relationship between characteristics of the input data and improved and reduced performance in our model. Based on that analysis, we identify and discuss key areas for further model development. To our knowledge, this is the first work that conducts an evaluation of real-time and retrospective infectious disease forecasts across the United States both with and without wastewater data and compared to other forecasting models.
Cite
@misc{johnsonBayesianGenerativeModeling2026,
title = {Bayesian Generative Modeling for Heterogeneous Wastewater Data Applied to COVID-19 Forecasting},
author = {{Johnson}, {Kaitlyn E.} and {Vega Yon}, {George G.} and {Brand}, {Samuel P. C.} and {Bernal Zelaya}, {Christian O.} and {Bayer}, {Damon} and {Volkov}, {Igor} and {Susswein}, {Zachary} and {Magee}, {Andrew} and {Gostic}, {Katelyn M.} and {English}, {Kayla M.} and {Ghinai}, {Isaac} and {Hamlet}, {Arran} and {Olesen}, {Scott W.} and {Pulliam}, {Juliet R. C.} and {Abbott}, {Sam} and {Morris}, {Dylan H.}},
year = {2026},
month = {feb},
publisher = {medRxiv},
doi = {10.64898/2026.02.23.26346887},
url = {https://doi.org/10.64898/2026.02.23.26346887},
}