Multi-factor normalisation of viral counts from wastewater improves the detection accuracy of viral disease in the community

Pellett, Cameron, Farkas, Kata, Williams, Rachel C., Wade, Matthew J., Weightman, Andrew J. ORCID: https://orcid.org/0000-0002-6671-2209, Jameson, Eleanor, Cross, Gareth and Jones, Davey L. 2024. Multi-factor normalisation of viral counts from wastewater improves the detection accuracy of viral disease in the community. Environmental Technology and Innovation 36 , 103720. 10.1016/j.eti.2024.103720

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Abstract

The detection of viruses (e.g. SARS-CoV-2, norovirus) in wastewater represents an effective way to monitor the prevalence of these pathogens circulating within the community. However, accurate quantification of viral concentrations in wastewater, proportional to human input, is constrained by a range of uncertainties, including (i) dilution within the sewer network, (ii) degradation of viral RNA during wastewater transit, (iii) catchment population and facility use, (iv) efficiency of viral concentration and extraction from wastewater, and (v) inhibition of amplification during the RT-qPCR step. Here, we address these uncertainties by investigating several potential normalisation factors including the concentration of ammonium and orthophosphate. A faecal indicator virus (crAssphage), and the recovery of the process-control viruses (murine norovirus and bacteriophage Phi6), used for quality control during the RT-qPCR step, were also considered. We found that multi-factor normalisation of SARS-CoV-2 RT-qPCR data was optimal using a combination of crAssphage, process-control virus recovery, and concentration efficiency to improve prediction accuracy relative to clinical test data. Using multi-normalised SARS-CoV-2 RT-qPCR data, we found a lasso regression model with random forest modelled residuals lowers the prediction error of positives by 46 %, compared to a single linear regression using raw data. This multi-normalised approach enables more accurate wastewater-based predictions of clinical cases up to five days in advance of clinical data, identifying trends in disease prevalence before clinical testing, and demonstrates the potential to improve viral pathogen detection for a range of currently monitored and emerging diseases.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences
Publisher:	Elsevier
ISSN:	2352-1864
Date of First Compliant Deposit:	15 July 2024
Date of Acceptance:	13 June 2024
Last Modified:	17 Jul 2024 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/170606

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