Lab Matters Fall 2024 | Page 137

APHL 2024 POSTER ABSTRACTS deconvolution pipeline in order to determine the relative frequency of influenza clade diversity in wastewater samples .

Presenter : Molly Hetherington-Rauth , molly . hetheringtonrauth @ state . co . us

Tracking the 2022-23 Influenza and RSV Seasons in Wisconsin : Comparison of Wastewater Surveillance Against Clinical Laboratory and Emergency Department Data .

I . Pray 1 , A . Roguet 2 , P . DeJonge 3 , D . Antkiewicz 4 , A . Bateman 4 , R . Westergaard 1 , M . Shafer 4 , Wisconsin Department of Health Services 1 , Wisconsin State Laboratory of Hygiene , University of Wisconsin-Madison 2 , Chicago Department of Public Health 3 , Wisconsin State Laboratory of Hygiene 4

During the 2022-23 respiratory season , influenza and respiratory syncytial virus ( RSV ) returned to pre-pandemic levels causing substantial morbidity and mortality in Wisconsin . Monitoring of SARS-CoV-2 through the Wisconsin Wastewater Surveillance Program previously demonstrated public health value through strong correlation with and lead-time over , clinical COVID-19 data . The Wisconsin State Laboratory of Hygiene ( WSLH ) developed and validated protocols for quantifying influenza A virus , influenza B virus and respiratory syncytial virus ( RSV ) from wastewater influent samples , which was first implemented as a pilot program during the 2021-22 season and was expanded for the 2022-23 season . The objective of our analysis was to determine if wastewater concentrations for influenza A virus and RSV could serve as an accurate and timely tool for monitoring influenza and RSV during the 2022-23 respiratory season .

Between August 2022 and March 2023 , WSLH conducted bi-weekly testing of 24-hr composite influent wastewater samples for influenza A virus and RSV from 20 wastewater treatment facilities across Wisconsin . Viral RNA was quantified by digital PCR after automated nanotrap concentration and extraction . Results were normalized to wastewater flow and sewershed population . For both viruses , the weekly statewide average wastewater concentration was compared against the weekly number of emergency department ( ED ) visits and percent positivity from clinical samples provided by the Wisconsin Clinical Laboratory Network . We evaluated correlation and lag between wastewater data , ED visits and clinical lab data for each virus using Spearman ’ s correlation for statewide summary data and Kendall correlation for city-level comparisons . Lag was evaluated through maximizing correlation coefficients across temporal offsets .

We observed significant positive correlations for both viruses when comparing wastewater concentrations against ED visits and clinical lab data . Statewide data showed a stronger correlation for influenza A virus than RSV , with correlations of 0.91 and 0.96 against ED visits and clinical lab data , respectively ; RSV correlations were moderate at 0.71 and 0.44 for wastewater concentrations against ED visits and clinical lab data , respectively . Correlations persisted at the city-level among Wisconsin ’ s three largest cities ( Kendall ’ s tau range = 0.50 – 0.63 for influenza and 0.30 – 0.49 for RSV ). Temporally , statewide wastewater levels aligned closely with ED visits and percent positivity for influenza ( lag = 0 and 1 week , respectively ), but showed a 4-week lag for RSV . This RSV lag was age-dependent , with wastewater concentrations lagging behind ED visits among 0 – 4-year-olds but coinciding with increases in adult ED visits .

Wastewater concentrations were strongly correlated with the percent positivity for clinical laboratory data and ED visits for influenza and RSV . However , we found no evidence that wastewater surveillance provided lead-time for either virus , which contrasts with its capability for SARS-CoV-2 . Despite this , wastewater surveillance served as a useful complement to existing surveillance during the 2022-23 season by confirming early increases and trajectory during the epidemics and providing robust community-level data to impact local decision-making .

Presenter : Ian Pray , ian . pray @ dhs . wisconsin . gov

Use of Wastewater Surveillance for the Detection and Monitoring of SARS-CoV-2 Variants

R . Choudhury 1 , R . Welsh 2 , D . Feistel 2 , D . Cornforth 2 , D . Nichols 3 , S . Morrison 2 , A . Mahale 4 , A . Kirby 2 , Association of Public Health Laboratories 1 , Centers for Disease Control and Prevention 2 , Chenega 3 , ASRT 4

Wastewater surveillance can provide public health information about the spread of disease within communities by detecting and quantifying the relative abundance of variants and mutations of known pathogens — like SARS-CoV-2 — shed by infected hosts into wastewater systems . Public health surveillance programs that rely on testing individuals for tracking the spread of disease are influenced by their subjects ’ access to healthcare , healthcareseeking behaviors and have a bias towards symptomatic and severe infections . Wastewater surveillance can complement their individual-focused approach as it samples at a community-level to capture both asymptomatic and symptomatic infections . Altogether , wastewater surveillance is an effective system for tracking the spread of disease and can be used to detect and monitor current variants and novel mutations .

The National Wastewater Surveillance System ( NWSS ) collects weekly wastewater samples from over 1,000 sites from 56 jurisdictions across the United States . As of December 2023 , NWSS has over 65,000 sequenced samples in our database for SARS- CoV-2 and recovered the relative abundance of known SARS-CoV-2 lineages from complex wastewater samples using a streamlined bioinformatics pipeline called Aquascope . The pipeline aligns sequence reads to the SARS-CoV-2 reference strain , identifies mutations using variant calling methods and calculates the relative abundance of known SARS-CoV-2 lineages using a deconvolution algorithm from the tool Freyja .

For this analysis , we tracked the lineages BA . 2.86 and JN . 1 , as well as their respective descendants , first detected in the US in July 2023 and September 2023 , respectively . When BA . 2.86 first emerged , the UShER global phylogenetic tree , used by Freyja for deconvolution , did not yet have the variant recognized . Utilizing NWSS ’ s systems , we were able to make a branch of the pipeline updated for BA . 2.86 and then re-analyzed all previously submitted wastewater samples . We identified the earliest sample containing BA . 2.86 , collected on July 30 , one day after the first clinical sample for the variant was collected from a patient in that sewershed . The JN . 1 variant , the most widely circulating SARS-CoV-2 variant as of January 2024 , is closely related to BA . 2.86 . NWSS has continued to track JN . 1 since its emergence and publicly reports the average relative proportions of SARS-COV-2 variants in wastewater at the