Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater. (preprint)

Wastewater surveillance has proven a key public health tool to understand a wide range of community health diseases and has proven to be especially critical to health departments throughout the SARS CoV-2 pandemic. The size of the population served by a wastewater treatment plant (WWTP) may limit the targeted insight about community disease dynamics. To investigate this concern, samples of wastewater were obtained at lift stations upstream of WWTPs within the sewer network. First, an online, semi-automatic time series model is fitted to the weekly measurements of WWTP samples to estimate the viral trend for the community and compared to the time series observations from the lift stations. Second, deviations from the WWTP trend are identified using an Exponentially Weighted Moving Average (EWMA) control chart. The analysis reveals that the lift stations display slightly different dynamics than the larger WWTP, highlighting the more granular insight gleaned from sampling sites which represent smaller populations. Discussion focuses on the use of our methods to support rapid public health decision-making based on additional, targeted samples in times of concern.

Crykey: Comprehensive Identification of SARS-CoV-2 Cryptic Mutations in Wastewater (preprint)

We present Crykey, a computational tool for identifying SARS-CoV-2 cryptic mutations from wastewater. While previous exist for identifying cryptic mutations in specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of tracking cryptic mutations across the entire genome and at scale. Crykey fills this gap and leverages the co-occurrence of single nucleotide variants on the same read combined with variant frequency information. We evaluated Crykey on SARS-CoV-2 sequences from 3175 wastewater samples and more than 14000 clinical samples. Our results are threefold, we show: 1) Crykey can accurately identify cryptic lineages that are rare or missing in existing databases ; 2) the emergence of cryptic lineage can be related to increased transmission rates in the communities, and 3) some cryptic lineages in wastewater mirror intra-host low frequency co-occurring variants in individuals. In summary, Crykey facilitates rapid and comprehensive identification of SARS-CoV-2 cryptic mutations in wastewater samples.

Enhanced Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater (preprint)

SARS-CoV-2 RNA shedding in stool enabled wastewater surveillance for the genetic material of the virus. With the emergence of novel variants of concern and interest it becomes increasingly important to track arrival and spread of these variants. However, most current approaches rely on the manually curated lists of mutations phenotypically associated with the variants of concern. The resulting data has many overlaps between distinct variants leading to less specific characterization of complex sample mixtures that result from wastewater monitoring. In our work we propose a simple and specific method for characterization of wastewater samples by introducing the concept of quasi-unique mutations. Our approach is data driven and results in earlier detection and higher resolution of variants of concern emergence patterns in wastewater data. ImportanceWastewater-based epidemiology has emerged as a powerful tool for public health response to the SARS-CoV-2 pandemic. As wastewater is a pooled, community sample of all persons contributing to the waste stream, there are several challenges in using sequencing information from wastewater samples to detect variants. Wastewater typically will consist of fragmented genomes from multiple, circulating variants. While it is straightforward to call the mutations present in a wastewater sample, it is more challenging to call the presence of variants that are defined by a set of characteristic mutations, particularly when mutations are shared among many circulating variants. Hence, we present a novel approach for screening for variants of concern in wastewater. Our computational approach introduces the concept of a "quasi-unique mutation" corresponding to a given PANGO lineage. We show that our method enables detection of the emergence of variants of concern in communities, providing a new approach for wastewater-based epidemiology of SARS-CoV-2.

Evaluating recovery, cost, and throughput of different concentration methods for SARS-CoV-2 wastewater-based epidemiology (preprint)

As the COVID-19 pandemic continues to affect communities across the globe, the need to contain the spread of the outbreaks is of paramount importance. Wastewater monitoring of the SARS-CoV-2 virus, the causative agent responsible for COVID-19, has emerged as a promising tool for health officials to anticipate outbreaks. As interest in wastewater monitoring continues to grow and municipalities begin to implement this approach, there is a need to further identify and evaluate methods used to concentrate SARS-CoV-2 virus RNA from wastewater samples. Here we evaluate the recovery, cost, and throughput of five different concentration methods for quantifying SARS-CoV-2 virus RNA in wastewater samples. We tested the five methods on six different wastewater samples. We also evaluated the use of a bovine coronavirus vaccine as a process control and pepper mild mottle virus as a normalization factor. Of the five methods we tested head-to-head, we found that HA filtration with bead beating performed the best in terms of sensitivity and cost. This evaluation can serve as a guide for laboratories establishing a protocol to perform wastewater monitoring of SARS-CoV-2. HighlightsO_LIFive methods for concentrating SARS-CoV-2 RNA from wastewater evaluated C_LIO_LIMethod performance characterized via recovery, cost, throughput, and variability C_LIO_LIHA filtration with bead beating had highest recovery for comparatively low cost C_LIO_LIBovine coronavirus, pepper mild mottle virus assessed as possible recovery controls C_LI