Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study

Dustin T Hill; Mohammed A Alazawi; E Joe Moran; Lydia J Bennett; Ian Bradley; Mary B Collins; Christopher J Gobler; Hyatt Green; Tabassum Z Insaf; Brittany Kmush; Dana Neigel; Shailla Raymond; Mian Wang; Yinyin Ye; David A Larsen

doi:10.1016/j.idm.2023.10.004

Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study

Infect Dis Model. 2023 Oct 31;8(4):1138-1150. doi: 10.1016/j.idm.2023.10.004. eCollection 2023 Dec.

Authors

Dustin T Hill¹, Mohammed A Alazawi², E Joe Moran^{2

3}, Lydia J Bennett^{2

3}, Ian Bradley⁴, Mary B Collins^{5

6}, Christopher J Gobler^{7

8}, Hyatt Green⁹, Tabassum Z Insaf^{2

10}, Brittany Kmush¹, Dana Neigel^{2

3}, Shailla Raymond^{2

3}, Mian Wang^{7

11}, Yinyin Ye⁴, David A Larsen¹

Affiliations

¹ Department of Public Health, Syracuse University, Syracuse, NY, 13244, USA.
² Center for Environmental Health, New York State Department of Health, Albany, NY, USA.
³ CDC Foundation, Atlanta, GA, USA.
⁴ Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA.
⁵ School of Marine and Atmospheric Sciences, Sustainability Studies Division, Stony Brook University, Stony Brook, NY, USA.
⁶ Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA.
⁷ New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, USA.
⁸ School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA.
⁹ Department of Environmental Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA.
¹⁰ Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, Rensselaer, NY, USA.
¹¹ Department of Civil Engineering, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY, USA.

Abstract

Background: The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data.

Methods: Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings.

Findings: Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]).

Interpretation: Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.

Keywords: COVID-19 hospitalizations; Forecasting; Prediction; SARS-CoV-2; Wastewater-based epidemiology.

Abstract

Grants and funding