Editorial: Wastewater-based epidemiological surveillance of respiratory pathogens
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,019 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».