Pathogen prioritisation for wastewater surveillance ahead of the Paris 2024 Olympic and Paralympic Games, France
Bibliographic record
Abstract
BackgroundWastewater surveillance is an effective approach to monitor population health, as exemplified by its role throughout the COVID-19 pandemic.AimThis study explores the possibility of extending wastewater surveillance to the Paris 2024 Olympic and Paralympic Games, focusing on identifying priority pathogen targets that are relevant and feasible to monitor in wastewater for these events.MethodsA list of 60 pathogens of interest for general public health surveillance for the Games was compiled. Each pathogen was evaluated against three inclusion criteria: (A) analytical feasibility; (B) relevance, i.e. with regards to the specificities of the event and the characteristics of the pathogen; and (C) added value to inform public health decision-making. Analytical feasibility was assessed through evidence from peer-reviewed publications demonstrating the detectability of pathogens in sewage, refining the initial list to 25 pathogens. Criteria B and C were evaluated via expert opinion using the Delphi method. The panel consisting of some 30 experts proposed five additional pathogens meeting criterion A, totalling 30 pathogens assessed throughout the three-round iterative questionnaire. Pathogens failing to reach 70% group consensus threshold underwent further deliberation by a subgroup of experts.ResultsSix priority targets suitable for wastewater surveillance during the Games were successfully identified: poliovirus, influenza A virus, influenza B virus, mpox virus, SARS-CoV-2 and measles virus.ConclusionThis study introduced a model framework for identifying context-specific wastewater surveillance targets for a mass gathering. Successful implementation of a wastewater surveillance plan for Paris 2024 could incentivise similar monitoring efforts for other mass gatherings globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".