PREreview of Integrating measles wastewater and clinical whole-genome sequencing enables high-resolution tracking of virus evolution and transmission

This study evaluates the utility of integrating wastewater genomic surveillance with clinical whole-genome sequencing to track measles virus during 2024–2025 South African outbreak. The main research question is: can the integration of wastewater genomic surveillance with routine clinical whole genome sequencing provide a higher resolution for tracking measles virus transmission and evolution compared to standard surveillance methods? The study tracks the "evolution and transmission" of the virus over several months, with a study design that is longitudinal. Main findings are that wastewater-integrated whole-genome surveillance expands the coverage and resolution of routine MeV monitoring and provides a scalable tool to advance measles control.

By utilizing a bioinformatic tool and amplicon sequencing, the researchers identified transmission links that were not seen with traditional clinical monitoring. The findings suggest that wastewater data can fill gaps in public health surveillance, providing a high resolution view of virus evolution. The authors propose a lowcost, scalable framework for monitoring measles at a population level. These results support the author's conclusion and show correlation between wastewater positivity rates and clinical IgM-positive tests as seen in their data that confirms that wastewater accurately showing disease activity.

Some strengths are high resolution data: moving from traditional N450 sequencing to whole-genome sequencing allowed the authors to uncover specific hidden transmission chains that standard surveillance would have missed. There is also tech innovation: the adaptation of a bioinformatics tool which was originally used for SARS-Cov-2, was used to identify measles variants in wastewater demonstrates a credible approach as its building on a framework that has already been used.

A limitation of the study, it noted the sensitivity of genome recovery in low-prevalence settings. While the study proved effective during an outbreak, the genome coverage decreases as the concentration of the measles virus in wastewater drops. This means that during periods of very low community transmission, wastewater surveillance might detect the presence of the virus but may not provide enough genomic data for high resolution phylogenetic tracking. 

 A major concern is sensitivity in low-prevalence areas: while integrated approach works well during a high-activity outbreak, the study doesn’t fully address the "limit of detection" for genomic recovery. Another major concern is that the study was conducted during a peak outbreak, making it easy to find large amounts of viral DNA. It remains unclear if this method would work when there are very few cases, and not sure if this tool is sensitive enough. As the outbreak wanes, the viral load in wastewater may become too fragmented to provide the complete genome coverage required for high resolution phylogenetic tracking.

The authors also note that the clinical test positivity rate was suppressed due to a rubella outbreak while wastewater positivity rates remained stable. This works to address if the wastewater system truly is more robust or if it may appear to be because the clinical system was overwhelmed. Furthermore, diving into concerns of whether WGS as a scalable tool is accurate, noting that there is no breakdown of costs to compare with the traditional method.

Furthermore, what makes a peer review fair and ethical is disclosing any potential conflicts of interest, giving constructive feedback that makes the research stronger. A potential bias to be aware of when reviewing this paper is confirmation bias if reviewers already believe that wastewater surveillance is a great tool. They may overlook gaps in data or accept conclusions without looking closely at the limitations.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.