Wastewater-based surveillance of SARS-CoV-2: Short-term projection (forecasting), smoothing and outlier identification using Bayesian smoothing

Day-to-day variation in the measurement of SARS-CoV-2 in wastewater can challenge public health interpretation. We assessed a Bayesian smoothing and forecasting method previously used for surveillance and short-term projection of COVID-19 cases, hospitalizations, and deaths. SARS-CoV-2 viral measure...

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Bibliographic Details
Published in:The Science of the total environment 2024-11, Vol.949, p.174937, Article 174937
Main Authors: Manuel, Douglas G., Saran, Gauri, Lee, Ivan, Yusuf, Warsame, Thomson, Mathew, Mercier, Élisabeth, Pileggi, Vince, McKay, R. Michael, Corchis-Scott, Ryland, Geng, Qiudi, Servos, Mark, Ikert, Heather, Dhiyebi, Hadi, Yang, Ivy M., Harvey, Bart, Rodenburg, Erin, Millar, Catherine, Delatolla, Robert
Format: Article
Language:English
Subjects:
COVID-19
Modelling
Predictive analytics
SARS-CoV-2
Statistics
Wastewater epidemiology
Wastewater surveillance
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Summary:Day-to-day variation in the measurement of SARS-CoV-2 in wastewater can challenge public health interpretation. We assessed a Bayesian smoothing and forecasting method previously used for surveillance and short-term projection of COVID-19 cases, hospitalizations, and deaths. SARS-CoV-2 viral measurement from the sewershed in Ottawa, Canada, sampled at the municipal wastewater treatment plant from July 1, 2020, to February 15, 2022, was used to assess and internally validate measurement averaging and prediction. External validation was performed using viral measurement data from influent wastewater samples from 15 wastewater treatment plants and municipalities across Ontario. Plots of SARS-CoV-2 viral measurement over time using Bayesian smoothing visually represented distinct COVID-19 “waves” described by case and hospitalization data in both initial (Ottawa) and external validation in 15 Ontario communities. The time-varying growth rate of viral measurement in wastewater samples approximated the growth rate observed for cases and hospitalization. One-week predicted viral measurement approximated the observed viral measurement throughout the assessment period from December 23, 2020, to August 8, 2022. An uncalibrated model showed underprediction during rapid increases in viral measurement (positive growth) and overprediction during rapid decreases. After recalibration, the model showed a close approximation between observed and predicted estimates. Bayesian smoothing of wastewater surveillance data of SARS-CoV-2 allows for accurate estimates of COVID-19 growth rates and one- and two-week forecasting of SARS-CoV-2 in wastewater for 16 municipalities in Ontario, Canada. Further assessment is warranted in other communities representing different sewersheds and environmental conditions. [Display omitted] •The final EpiNow2 model smoothed historic wastewater measures & identified weekly periodicity and clinically important waves.•Initially the model had issues with over- and under-prediction at extreme growth rates; corrected with recalibration.•Wastewater growth estimates generated by the model were consistent with growth estimated by hospitalizations and cases.•The outlier detection method performed well, with results closely aligned with anticipated levels of statistical uncertainty.
ISSN:0048-9697
1879-1026
1879-1026
DOI:10.1016/j.scitotenv.2024.174937