PREreview of Wastewater surveillance to track a generational scale outbreak of measles in Ontario, Canada, February - November, 2025

Summary

This manuscript presents a wastewater surveillance case study from southwestern Ontario during the large 2025 measles outbreak. The authors monitored measles virus RNA in wastewater from the Leamington Pollution Control Centre, compared wastewater signals with reported clinical cases, assessed measles virus partitioning across wastewater fractions, tested wild-type versus vaccine-genotype detection, and estimated shedding rates using hospital-effluent sampling from Windsor Regional Hospital.

The study finds that wastewater measles signal correlated strongly with reported cases but did not provide early warning when analyzed by epidemiological week. The work also suggests that measles RNA is broadly present in liquid wastewater fractions but most concentrated in solids by mass, that vaccine-genotype signal can be detected after targeted MMR vaccination activity, and that hospital-effluent measurements may support rough case estimation. This work moves the field forward by providing practical, real-world evidence for measles wastewater surveillance in a rural/septic-influenced sewershed.

Major Issues

1. The conclusion that wastewater surveillance is an “accurate and unbiased” measure of measles incidence should be softened. The study is based mainly on one outbreak-affected sewershed with septic hauling and likely underreported clinical cases. “Independent and useful correlate of reported incidence” may be more defensible unless additional validation sites are included.

2. The statistical models have small sample sizes, especially the truncated septic-hauling GLM with n=14, and residual diagnostics showed mild heteroskedasticity. Please expand the uncertainty discussion and consider simpler sensitivity analyses, confidence intervals around model predictions, or nonparametric comparisons.

3. The shedding-rate estimates are highly uncertain. They rely on passive samplers, inferred hospital effluent concentrations, estimated flow from water use, few sampling dates, and hospitalized patients who may not represent community cases. The case-estimation results should be framed as exploratory rather than quantitative validation.

4. The partitioning experiment appears to rely on one wastewater sample with technical/biological replicates from the same setting. Please clarify the replicate structure and avoid generalizing too strongly about optimal sample fractions without testing additional sites, seasons, and wastewater characteristics.

5. The vaccine-genotype analysis is interesting but currently quite qualitative. Please provide assay performance details such as limit of detection, specificity in mixed wild-type/vaccine backgrounds, inhibition controls, and how binary positives were defined.

6. The manuscript should more clearly distinguish delays caused by biology, sampling frequency, weekly aggregation, and septic hauling. Higher-resolution daily analyses, even exploratory, would help assess whether the lack of lead time is intrinsic to measles wastewater surveillance or specific to this setting.

Minor Issues

1. Please add or clarify a data and code availability statement, including wastewater measurements, case counts where shareable, model code, and assay metadata.

2. Define abbreviations at first use, including WS, WWTP, LPCC, WRH, PMMoV, TLCC, and GLM.

3. The phrase “generational scale outbreak” is memorable but should be defined quantitatively or replaced with a more standard epidemiological description.

4. Please use consistent terminology: “wild-type” versus “wildtype,” and “RT-qPCR” versus “qRT-PCR.”

5. Figure legends would benefit from clearer sample sizes, date ranges, and whether values are raw samples, weekly means, or normalized measures.

6. The limitations section is useful but could more explicitly mention single-site generalizability, under-ascertainment of clinical cases, and uncertainty from septic-hauling timing.

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.