Pathogen prioritisation for wastewater surveillance ahead of the Paris 2024 Olympic and Paralympic Games, France
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it