Modelling patterns of SARS-CoV-2 circulation in the Netherlands, August 2020-February 2022, revealed by a nationwide sewage surveillance program

Abstract

SUMMARY Background Surveillance of SARS-CoV-2 in wastewater offers an unbiased and near real-time tool to track circulation of SARS-CoV-2 at a local scale, next to other epidemic indicators such as hospital admissions and test data. However, individual measurements of SARS-CoV-2 in sewage are noisy, inherently variable, and can be left-censored. Aim We aimed to infer latent virus loads in a comprehensive sewage surveillance program that includes all sewage treatment plants (STPs) in the Netherlands and covers 99.6% of the Dutch population. Methods A multilevel Bayesian penalized spline model was developed and applied to estimate time- and STP-specific virus loads based on water flow adjusted SARS-CoV-2 qRT-PCR data from 1-4 sewage samples per week for each of the >300 STPs. Results The model provided an adequate fit to the data and captured the epidemic upsurges and downturns in the Netherlands, despite substantial day-to-day measurement variation. Estimated STP virus loads varied by more than two orders of magnitude, from approximately 1012 (virus particles per 100,000 persons per day) in the epidemic trough in August 2020 to almost 1015 in many STPs in January 2022. Epidemics at the local levels were slightly shifted between STPs and municipalities, which resulted in less pronounced peaks and troughs at the national level. Conclusion Although substantial day-to-day variation is observed in virus load measurements, wastewater-based surveillance of SARS-CoV-2 can track long-term epidemic progression at a local scale in near real-time, especially at high sampling frequency.

Wastewater-based reproduction numbers and projections of COVID-19 cases in three areas in Japan, November 2021 to December 2022

“…The strength of these methods lies in the functional flexibility of models, which allows for the smoothing of noisy data, e.g. penalised splines [ 16 ], neural networks [ 14 , 17 ]. These studies primarily focused on short-term forecasting and aimed at providing near real-time estimates [ 15 , 16 ].…”

“…penalised splines [ 16 ], neural networks [ 14 , 17 ]. These studies primarily focused on short-term forecasting and aimed at providing near real-time estimates [ 15 , 16 ]. However, a drawback of non-mechanistic models is that they do not necessarily provide biological interpretations, and thus the outputs from such analyses are difficult to use for policy guidance with further scenario analysis.…”

“…Previous studies have attempted to deal with the noise in wastewater data by using statistical or machinelearning-based approaches [14][15][16][17]. The strength of these methods lies in the functional flexibility of models, which allows for the smoothing of noisy data, e.g.…”

Wastewater-based reproduction numbers and projections of COVID-19 cases in multiple cities in Japan, 2022

“…Previous studies have attempted to deal with the noise in wastewater data by using statistical or machine-learning based approaches [14][15][16][17]. The strength of these methods lies in the functional flexibility of models, which allows for the smoothing of noisy data (e.g., penalized splines [16], neural network [14,17]). These studies primarily focused on short-term forecasting and were successful in providing near real-time estimates [15,16].…”

“…The strength of these methods lies in the functional flexibility of models, which allows for the smoothing of noisy data (e.g., penalized splines [16], neural network [14,17]). These studies primarily focused on short-term forecasting and were successful in providing near real-time estimates [15,16]. However, a drawback of non-mechanistic models is that they do not necessarily provide biological interpretations, and thus the outputs from such analyses are difficult to use for policy guidance with further scenario analysis.…”