PulseNet International Survey on the Implementation of Whole Genome Sequencing in Low and Middle-Income Countries for Foodborne Disease Surveillance
Bibliographic record
Abstract
PulseNet International (PNI) is a global network of 88 countries who work together through their regional and national public health laboratories to track foodborne disease around the world. The vision of PNI is to implement globally standardized surveillance using whole genome sequencing (WGS) for real-time identification and subtyping of foodborne pathogens to strengthen preparedness and response and lower the burden of disease. Several countries in North America and Europe have experienced significant benefits in disease mitigation after implementing WGS. To broaden the routine use of WGS around the world, challenges and barriers must be overcome. We conducted this study to determine the challenges and barriers countries are encountering in their attempts to implement WGS and to identify how PNI can provide support to improve and become a better integrated system overall. A survey was designed with a set of qualitative questions to capture the status, challenges, barriers, and successes of countries in the implementation of WGS and was administered to laboratories in Africa, Asia-Pacific, Latin America and the Caribbean, and Middle East. One-third of respondents do not use WGS, and only 8% reported using WGS for routine, real-time surveillance. The main barriers for implementation of WGS were lack of funding, gaps in expertise, and training, especially for data analysis and interpretation. Features of an ideal system to facilitate implementation and global surveillance were identified as an all-in-one software that is free, accessible, standardized and validated. This survey highlights the minimal use of WGS for foodborne disease surveillance outside the United States, Canada, and Europe to date. Although funding remains a major barrier to WGS-based surveillance, critical gaps in expertise and availability of tools must be overcome. Opportunities to seek sustainable funding, provide training, and identify solutions for a globally standardized surveillance platform will accelerate implementation of WGS worldwide.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".