ABSTRACT
The COVID-19 pandemic has accelerated the development and adoption of wastewater-based epidemiology. Wastewater samples can provide genomic information for detecting and assessing the spread of SARS-CoV-2 variants in communities and for estimating important epidemiological parameters such as the growth advantage of the variant. However, despite demonstrated successes, epidemiological data derived from wastewater suffers from potential biases. Of particular concern are differential shedding profiles that different variants of concern exhibit, because they can shift the relationship between viral loads in wastewater and prevalence estimates derived from clinical cases. Using mathematical modeling, simulations, and Swiss surveillance data, we demonstrate that this bias does not affect estimation of the growth advantage of the variant and has only a limited and transient impact on estimates of the effective reproduction number. Thus, population-level epidemiological parameters derived from wastewater maintain their advantages over traditional clinical-derived estimates, even in the presence of differential shedding among variants.
Subject(s)
COVID-19ABSTRACT
The COVID-19 pandemic has accelerated the development and adoption of wastewater-based epidemiology. Wastewater samples can provide genomic information for detecting and assessing the spread of SARS-CoV-2 variants in communities and for estimating important epidemiological parameters such as the growth advantage of the variant. However, despite demonstrated successes, epidemiological data derived from wastewater suffers from potential biases. Of particular concern are differential shedding profiles that different variants of concern exhibit, because they can shift the relationship between viral loads in wastewater and prevalence estimates derived from clinical cases. Using mathematical modeling, simulations, and Swiss surveillance data, we demonstrate that this bias does not affect estimation of the growth advantage of the variant and has only a limited and transient impact on estimates of the effective reproduction number. Thus, population-level epidemiological parameters derived from wastewater maintain their advantages over traditional clinical-derived estimates, even in the presence of differential shedding among variants.
Subject(s)
COVID-19ABSTRACT
Throughout the global COVID-19 pandemic, SARS-CoV-2 genetic variants of concern (VOCs) have repeatedly and independently arisen. VOCs are characterized by increased transmissibility, increased virulence, or reduced neutralization by antibodies obtained from prior infection or vaccination. Tracking the introduction and transmission of VOCs relies on sequencing, typically whole-genome sequencing of clinical samples. Wastewater surveillance is increasingly used to track the introduction and spread of SARS-CoV-2 variants through sequencing approaches. Here, we adapt and apply a rapid, high-throughput method for detection and quantification of the frequency of two deletions characteristic of the B.1.1.7, B.1.351, and P.1 VOCs in wastewater. We further develop a statistical approach to analyze temporal dynamics in drop-off RT-dPCR assay data to quantify transmission fitness advantage, providing data similar to that obtained from clinical samples. Digital PCR assays targeting signature mutations in wastewater offer near real-time monitoring of SARS-CoV-2 VOCs and potentially earlier detection and inference on transmission fitness advantage than clinical sequencing.
Subject(s)
Severe Acute Respiratory Syndrome , COVID-19 , SeizuresABSTRACT
The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. Here we show that the dynamics of SARS-CoV-2 RNA in wastewater can be used to estimate Re in near real-time, independent of clinical data and without associated biases stemming from clinical testing and reporting strategies. The method to estimate Re from wastewater is robust and applicable to data from different countries and wastewater matrices. The resulting estimates are as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re. To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens.
Subject(s)
DeathABSTRACT
Wastewater-based epidemiology (WBE) has been shown to coincide with, or anticipate, confirmed COVID-19 case numbers. During periods with high test positivity rates, however, case numbers may be underreported, whereas wastewater does not suffer from this limitation. Here we investigated how the dynamics of new COVID-19 infections estimated based on wastewater monitoring or confirmed cases compare to true COVID-19 incidence dynamics. We focused on the first pandemic wave in Switzerland (February to April, 2020), when test positivity ranged up to 26%. SARS-CoV-2 RNA loads were determined 2-4 times per week in three Swiss wastewater treatment plants (Lugano, Lausanne and Zurich). Wastewater and case data were combined with a shedding load distribution and an infection-to-case confirmation delay distribution, respectively, to estimate incidence dynamics. Finally, the estimates were compared to reference incidence dynamics determined by a validated compartmental model. Incidence dynamics estimated based on wastewater data were found to better track the timing and shape of the reference infection peak compared to estimates based on confirmed cases. In contrast, case confirmations provided a better estimate of the subsequent decline in infections. Under a regime of high-test positivity rates, WBE thus provides critical information that is complementary to clinical data to monitor the pandemic trajectory.
Subject(s)
COVID-19ABSTRACT
The SARS-CoV-2 lineages B.1.1.7 and 501.V2, which were first detected in the United Kingdom and South Africa, respectively, are spreading rapidly in the human population. Thus, there is an increased need for genomic and epidemiological surveillance in order to detect the strains and estimate their abundances. Here, we report a genomic analysis of SARS-CoV-2 in 48 raw wastewater samples collected from three wastewater treatment plants in Switzerland between July 9 and December 21, 2020. We find evidence for the presence of several mutations that define the B.1.1.7 and 501.V2 lineages in some of the samples, including co-occurrences of up to three B.1.1.7 signature mutations on the same amplicon in four samples from Lausanne and one sample from a Swiss ski resort dated December 9 - 21. These findings suggest that the B.1.1.7 strain could be detected by mid December, two weeks before its first verification in a patient sample from Switzerland. We conclude that sequencing SARS-CoV-2 in community wastewater samples may help detect and monitor the circulation of diverse lineages.