WBEready – Wastewater-based epidemiology and preparedness: research needs for an adaptive monitoring in the Public Health Service
Bibliographic record
Abstract
Wastewater-based epidemiology (WBE) was able to provide early indicators of an infectious event during the COVID-19 pandemic in Germany, complementing individual testing for outbreak detection. WBE also enables regional surveillance and can assist public health systems (PHS) in evaluating the effectiveness of infectious disease control measures. The German Federal Ministry of Health (BMG) sees great benefit for the PHS in further development of WBE, even beyond COVID- 19. However, in order to bring the full potential of WBE into broad application, new analytical, technical, epidemiological, and institutional research questions need to be addressed. The overall objective of the project WBEready applied for herewith is the scientific extension of a roadmap for the development of a future-oriented WBE with adaptive monitoring capacities in the PHS. To this end, the current screening in the AMELAG (Wastewater monitoring for epidemiological situation surveillance) project is to be expanded to include numerous other pathogens in addition to SARS-CoV-2, including circulating and emerging human pathogenic viruses as well as antimicrobial resistances, and the foundations are to be laid by answering open research questions in order to prepare for new requirements in the PHS in a targeted and cost-efficient manner (preparedness). Central questions are if new target parameters are suitable for the WBE set-up, which requirements are epidemiologically necessary and how WBE can support the institutional work of the PHS. In order to address the relevant research questions, the association area of Emschergenossenschaft and Lippeverband with focus on the Ruhr area will be investigated as a real laboratory with regard to the usable wastewater infrastructure (agglomerations, sewer networks, hospitals, wastewater treatment plants of different size classes) under consideration of socio-economic parameters. The interdisciplinary WBEready consortium has the expertise, experience and networking to answer these research questions. Already in the first pandemic wave in April 2020, sampling for SARS-CoV-2 wastewater monitoring was carried out, the first publication in the Coronapandemic in Germany was published, own laboratory capacities were built up, and with the BMBF research project COVIDready and further projects in the Emscher-Lippe region, a close cooperation with the actors at the municipal, state and federal level essential for a deployment of WBE was established. Publication History Article published online: 10 April 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".