Shiga Toxin-Producing Escherichia coli (STEC) in Developing Countries: A 10-Year Review with Global Perspective
Bibliographic record
Abstract
In the past two decades, Shiga toxin-producing Escherichia coli (STEC) has been responsible for multiple large-scale outbreaks worldwide, affecting thousands of individuals. While surveillance systems in developed countries such as the United States, the United Kingdom, Europe, Australia, Japan, and Canada are well-established, data on STEC prevalence in developing nations remain sparse, partly due to the absence of well-structured molecular diagnostic networks or surveillance systems. This review analyzed 250 studies published between 2014 and 2024 across 39 developing countries in Africa, Asia, Latin America, and the Caribbean, yielding 8986 STEC isolates. Detailed serogroup and serotype data were available for 55.9% of these, with O111, O157, and O26 being most common in humans. In animals, O157:H7 was most frequent, while food isolates mirrored global trends with O157 and O111 dominance. Notably, O145, a serogroup frequently reported in the U.S. and Europe, was absent from the ‘’Top Seven’’ serogroups. Shiga toxin subtypes stx1a and stx2a were most prevalent in human cases. In animal isolates, stx2e was the most prevalent subtype, while stx2c was most commonly found in food samples. We recommend establishing reference laboratories in these regions to improve data quality, strengthen monitoring efforts, and reduce the burden of STEC infections 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".