Editorial: Wastewater-based epidemiological surveillance of respiratory pathogens
Bibliographic record
Abstract
This research topic showcases studies that highlight the emergence of the surveillance of respiratory pathogens from wastewater samples and also the potential challenges ahead.Wastewater-based surveillance brings many potential benefits to public health activities, notably to complement (but not replace) other data sources as de Melo et al. show with student absenteeism data. One of the main goals of wastewater surveillance is to link the pathogen concentration in wastewater with hospitalizations associated with the pathogen. But, as Kadonsky et al. hint, the pathogen concentration in wastewater may be affected by factors unrelated to its epidemiology and perturb the wastewater concentration/hospitalization relationship. Normalization techniques (such as the one proposed by Dhiyebi et al.) are still being explored to account for exogenous effects to correct nonepidemiological effects on the wastewater signal.Fecal shedding kinetics for an infected individual, a cornerstone of WBE, is poorly known for most pathogens. Even for the well-studied SARS-CoV-2, we don't know how (or even if) fecal shedding changed as the population got exposed to successive variants and different vaccines. In that spirit, Rioux et al. propose a clinical-data-driven method to correct initial assumptions about fecal kinetics.Laboratory methods that accurately quantify viral concentration in wastewater are still in their early stages. We are probably several years away from a gold-standard laboratory method, assuming that "gold standard" even makes sense, given the diversity of the wastewater matrix across different sampling situations. In this Research Topic, the studies by As an emerging field, WBE must find its place beside more established public health surveillance programs. There is probably no one-size-fits-all solution, but the study by Clark et al. can be helpful as they share a comprehensive framework that has been implemented in a large North American jurisdiction.The COVID-19 pandemic showed the unexpected utility of WBE for respiratory pathogens. Leveraging this success, many jurisdictions are expanding their WBE to other respiratory pathogens, notably seasonal influenza (de Melo et al.) and RSV, historically the most burdensome respiratory diseases. Prioritizing and right-sizing WBE to new pathogens will be key to improving public health but may also be challenging given the specificities of each jurisdiction. To support such efforts, Gentry et al. propose a ranking system that was applied in a large North American urban center.The choice regarding the geographical location of sampling sites can provide different spatial levels for epidemiological analyses. Sampling wastewater at wastewater treatment plants is a popular and practical choice to monitor the prevalence of a given infection at the (sub)municipal level. However, more focused sampling can bring unprecedented insights into fine-grained transmission patterns at key "hot spots". For example, the study by Corchis-Scott et al. in this Research Topic samples university residences.Clearly, WBE for respiratory pathogens is still in its infancy, and many knowledge gaps need to be filled. This research topic is a step in that direction. Given the recent expansion of WBE in many different jurisdictions worldwide, it is exciting to see research in this new field that promises to improve public health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".