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

  1. Sipho Gwala
  2. Joshua I. Levy
  3. Victor Mabasa
  4. Kathleen Subramoney
  5. Nkosenhle L. Ndlovu
  6. Chris Kent
  7. Maryam Ahmadi Jeshvaghane
  8. Praneeth Gangavarapu
  9. Menzi Sikakane
  10. Natasha Singh
  11. Mantshali Motloung
  12. Lethabo Monametsi
  13. Lebohang Rabotapi
  14. Emmanuel Phalane
  15. Mokgaetji Macheke
  16. Fiona Els
  17. Chenoa Sankar
  18. Tshepo Motsamai
  19. Sibonginkosi Maposa
  20. Nishi Prabdial-Sing
  21. Joshua Quick
  22. Kristian G. Andersen
  23. Kerrigan McCarthy
  24. Mukhlid Yousif

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Abstract

Measles outbreaks have surged globally in recent years, but current surveillance systems have limited capacity to monitor measles virus (MeV) transmission and evolution at population scale. Although MeV can be detected in wastewater, the public health potential of wastewater genomic surveillance for MeV remains largely unexplored. Here, we deploy sensitive, low-cost MeV wastewater genomic surveillance combining virus concentration, whole-genome amplicon sequencing, and bioinformatic analysis alongside routine clinical genomic surveillance during the 2024-25 outbreak in South Africa. Integrated phylogenetic analyses of wastewater and clinical MeV genomes revealed previously undetected interprovincial spread and transmission links not captured by standard N450 sequencing. Our findings demonstrate that wastewater-integrated whole-genome surveillance expands the coverage and resolution of routine MeV monitoring and provides a scalable tool to advance measles control and elimination efforts.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/20058572.

    Summary

    This preprint presents an integrated wastewater and clinical whole-genome sequencing approach for measles virus surveillance during the 2024-2025 outbreak in South Africa. The authors analyzed 4,502 wastewater samples, identified measles RNA in 395, sequenced a subset, and combined these data with clinical whole-genome sequences to track genotypes B3 and D8. The study shows that wastewater measles positivity broadly reflected clinical case patterns, that wastewater sequencing recovered genotype shifts over time, and that whole-genome sequencing provided substantially higher resolution than standard N450 sequencing for reconstructing geographic spread and transmission links.

    This work moves the field forward by demonstrating that wastewater-based genomic surveillance can complement case-based measles surveillance, especially where clinical sampling is uneven or sequencing coverage is limited. The manuscript is timely and useful because measles surveillance needs scalable tools that can detect transmission at population level and help distinguish local spread from multiple introductions.

    Positive Feedback

    The study is strong in its integration of laboratory, epidemiological, and phylogenetic approaches. I especially appreciated the comparison of wastewater and clinical surveillance, the validation of Freyja-based genotype assignment, and the direct comparison between whole-genome and N450-based analyses. The public health motivation is clear, and the work provides a practical model for expanding genomic surveillance beyond SARS-CoV-2 into vaccine-preventable diseases.

    Major Issues

    1. The representativeness of wastewater sequencing should be clarified. Although the study is framed as national surveillance, most wastewater sequence data appear to come from Gauteng. The authors should provide a clearer breakdown of sequenced and successfully genotyped wastewater samples by province, district, site type, and time period. This would help readers assess how strongly the conclusions apply across South Africa versus mainly to Gauteng.

    2. Only a subset of positive wastewater samples was sequenced, and only 51/395 positives had sufficient coverage for genotype prevalence estimates. The manuscript would be strengthened by comparing viral load, site, date, and geography between sequenced/analyzable and non-analyzable positives. This would make it easier to evaluate whether genotype prevalence estimates could be biased toward higher viral-load samples or better-covered sites.

    3. The transmission-inference language could be more cautious. The phylogenetic results are compelling, but phrases implying directionality, such as wastewater sequences descending from clinical sequences or evidence of interprovincial spread, should be supported with clearer uncertainty estimates. Please consider adding bootstrap/support values, temporal-signal diagnostics, sensitivity analyses using different coverage thresholds, and a clearer discussion of how incomplete clinical and global background sampling may affect inferred transmission links.

    4. The comparison between whole-genome sequencing and N450 sequencing is important, but the fairness of the comparison should be described more explicitly. For example, were the same sequences, filters, and phylogenetic methods used after masking to N450? How were sequences without N450 coverage handled? The AU and parsimony-score analyses support the authors' conclusion, but a short explanation of their interpretation for non-phylogenetics readers would improve clarity.

    5. The wastewater-clinical correlation analyses could be expanded. District-level wastewater TPR correlated strongly with case counts, while temporal clinical and wastewater measures were weaker or not correlated depending on the clinical metric. Lagged analyses, adjustment for testing volume, population/catchment size, and the wastewater concentration-method change in week 41 of 2024 would help clarify how wastewater signals should be interpreted operationally.

    Minor Issues

    1. There is a date inconsistency: the methods and results describe samples through August 31, 2025, but some figure captions refer to October 1, 2024 to September 30, 2025. Please check and harmonize these dates.

    2. Please define abbreviations clearly at first use, especially WES, WWTP, TPR, SNV, CMH, PS, and AU.

    3. The clinical whole-genome sequencing subset of 39 samples should be described in more detail. Were these selected by geography, viral load, storage availability, genotype, or epidemiological relevance?

    4. The change from concentration method C to method A is important. A brief statement on whether this affected longitudinal comparability of wastewater viral load or positivity would help.

    5. The figures contain a lot of information. Adding clearer site labels, catchment/population context, and color-blind-accessible genotype colors would improve readability.

    6. The data and code availability statements are helpful. Please include a specific repository release, commit hash, or analysis environment so the results can be reproduced more easily.

    7. The discussion would benefit from a short dedicated limitations paragraph covering Gauteng-heavy sequencing, incomplete clinical sequencing, low-viral-load wastewater failures, and uncertainty in phylogenetic transmission inference.

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

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

  6. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19589236.

    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.

  7. Version published to 10.64898/2026.02.21.26346782 on medRxiv