Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates

Rochelle H Holm; Grzegorz A Rempala; Boseung Choi; J Michael Brick; Alok R Amraotkar; Rachel J Keith; Eric C Rouchka; Julia H Chariker; Kenneth E Palmer; Ted Smith; Aruni Bhatnagar

doi:10.1038/s43856-024-00494-y

Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates

Commun Med (Lond). 2024 Apr 9;4(1):70. doi: 10.1038/s43856-024-00494-y.

Authors

Rochelle H Holm^#¹, Grzegorz A Rempala^#², Boseung Choi^#^{2

3

4}, J Michael Brick⁵, Alok R Amraotkar¹, Rachel J Keith¹, Eric C Rouchka^{6

7}, Julia H Chariker^{6

7}, Kenneth E Palmer^{8

9}, Ted Smith¹, Aruni Bhatnagar¹⁰

Affiliations

¹ Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA.
² Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, 43210, USA.
³ Division of Big Data Science, Korea University, Sejong, South Korea.
⁴ Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea.
⁵ Westat, Inc., Rockville, MD, 20850, USA.
⁶ Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, 40202, USA.
⁷ KY INBRE Bioinformatics Core, University of Louisville, Louisville, KY, 40202, USA.
⁸ Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY, 40202, USA.
⁹ Department of Pharmacology and Toxicology, School of Medicine, University of Louisville, Louisville, KY, 40202, USA.
¹⁰ Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA. aruni.bhatnagar@louisville.edu.

^# Contributed equally.

Abstract

Background: Despite wide scale assessments, it remains unclear how large-scale severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates.

Methods: We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible ( $S$ ), vaccinated ( $V$ ), variant-specific infected ( $I_{1}$ and $I_{2}$ ), recovered ( $R$ ), and seropositive ( $T$ ) model ( $S V I_{2} R T$ ) tracked prevalence longitudinally. This was related to wastewater concentration.

Results: Here we show the 64% county vaccination rate translate into about a 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence is a 24-fold increase of infection counts, which correspond to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration have the strongest correlation (r = 0.95) at 1 week lag.

Conclusions: Our study underscores the importance of continuing environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.

Plain language summary

It is unclear how large-scale COVID-19 vaccination impacts wastewater concentration or overall disease burden. Here, we developed a mathematical surveillance model that allows estimation of overall vaccine impact based on the amount of SARS-CoV-2 in wastewater, seroprevalence and the number of cases admitted to hospitals between April 2021–August 2021 in Jefferson County, Kentucky USA. We found that a 64% vaccination coverage correlated to a 61% decrease in COVID-19 cases. The emergence of the SARS-CoV-2 Delta variant during the time of the surveillance directly correlated with a sharp increase in infection incidence as well as viral counts in wastewater. The hospitalization burden was closely reflected by the viral count found in the wastewater, indicating that post-vaccine environmental surveillance can be an effective method of estimating changing disease prevalence in future pandemics.

Grants and funding

P20 GM103436/GM/NIGMS NIH HHS/United States