As SARS-CoV-2 becomes an endemic agent worldwide, communities and health agencies must figure out how to identify novel variants and track infection rates in ways that are logistically sustainable without sacrificing sensitivity or accuracy. Fortunately, wastewater-based epidemiology (WBE) has proven its ability to play an important role in monitoring COVID-19 outbreaks, and Droplet Digital PCR (ddPCR) has become a cornerstone method for ongoing wastewater-based disease surveillance. In this article, we review the role of wastewater-based testing in community health before, during, and beyond the COVID-19 pandemic, and we explore why ddPCR technology is ideally suited for this application as it continues to expand and evolve.

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Epidemiological water testing has been in place since long before the pandemic. Prior to its widespread use in SARS-CoV-2 monitoring, water-based surveillance provided insights into many community health issues. Scientists and public health officials have used it to monitor levels of pharmaceutical or industrial waste in lakes and ponds, contaminants in drinking water, and pathogens including norovirus, Zika virus, and methicillin-resistant Staphylococcus aureus (MRSA) in wastewater.

Wastewater surveillance is particularly valuable for tracking community spread of infectious diseases such as COVID-19 because it serves as an early warning system. Infected individuals can shed viral RNA into wastewater days before becoming symptomatic, and some may never develop symptoms. Other cases may go undiagnosed because patients are hesitant or unable to pursue clinical testing. Unlike other surveillance methods, wastewater testing accounts for these cases, allowing scientists to generate a real-time picture that captures all infections throughout the community. 

Why is ddPCR Technology Ideal for Wastewater Surveillance?

While quantitative PCR (qPCR) is the gold standard diagnostic for COVID-19 in patients, ddPCR technology quickly emerged as the favored method for wastewater surveillance testing. ddPCR technology works to quantify viral RNA in wastewater by fractionating a sample into 20,000 individual nanoliter-sized droplets. The DNA or RNA in the sample is distributed throughout these droplets, meaning that some will feature the target sequence while others won’t. PCR amplification is then carried out within each droplet, and fluorescence is measured at the end of the process. By counting the number of target sequence–positive droplets (which fluoresce) versus negative droplets (which don’t) and adjusting for distribution effects, the system determines the concentration of the pathogen in the original wastewater sample. This direct counting approach provides absolute quantification, which eliminates the need for a standard curve, reducing the effort and human variability associated with maintaining standards.

In addition to providing absolute quantification, this droplet partitioning makes ddPCR technology particularly apt for the unique challenges of WBE because it allows the enrichment of rare targets in samples. The technique is far more sensitive to low levels of RNA or DNA than bulk methods. One study found this sensitivity to be sufficient to detect SARS-CoV-2 from just one person out of 10,000. This ability could be critical in detecting potential outbreaks and stopping them before they spread (Hart and Halden 2020).

Wastewater is also full of chemicals and biological materials that could disrupt a PCR reaction. Typically, this would necessitate a careful balancing act of purifying wastewater adequately without compromising the sample yield. However, unlike qPCR data, ddPCR data are analyzed by endpoint detection, so inhibitors that reduce the yield or delay amplification of the target sequence are much more tolerated and less likely to impact results.

The Challenge of Mutations

ddPCR methods are also well-suited to detecting any known novel SARS-CoV-2 variants in wastewater samples. In a single assay, ddPCR technology can accurately and simultaneously discriminate and quantify even small concentrations of multiple variants in wastewater. This level of sensitivity — and efficiency — is critical for the early detection of outbreaks involving new variants.

Because keeping abreast of variant spread across communities is key in guiding healthcare responses and public policy, detecting and discriminating among variants will likely be a core focus of WBE approaches as SARS-CoV-2 continues to mutate and spread over time. 

A Lasting Role in Wastewater-Based Epidemiology

The U.S. CDC’s National Wastewater Surveillance System (NWSS) is rapidly growing as a dedicated effort to track SARS-CoV-2 throughout the U.S., with more than 700 sites contributing data. CDC guidance endorses ddPCR technology as a preferred approach to wastewater testing because of its high tolerance to inhibitors (Centers for Disease Control and Prevention 2022).

ddPCR technology — which has been a vital tool across basic research, clinical diagnostics, and biopharmaceutical manufacturing for over a decade —has now, over the course of the COVID-19 pandemic, demonstrated its widespread value, and its promise as an ongoing solution, in community health surveillance. It will undoubtedly continue to play a role as WBE efforts expand to meet other challenges. From detecting and tracking new SARS-CoV-2 variants to monitoring the spread of influenza (preprint: Wolfe et al. 2022) and diseases that may arise in the future, ddPCR technology is firmly in place as an essential tool in the public health effort to keep communities safer.

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References

Centers for Disease Control and Prevention. (2022). Wastewater surveillance testing methods. cdc.gov/healthywater/surveillance/wastewater-surveillance/testing-methods.html, accessed August 9, 2022.

Hart OE and Halden RU (2020). Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: Feasibility, economy, opportunities and challenges. Sci Total Environ 730, 138875.

Wolfe MK et al. (2022). Wastewater-based detection of an influenza outbreak. MedRxiv. Preprint. doi.org/10.1101/2022.02.15.22271027, accessed August 9, 2022.

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