Laboratory-acquired infections and pathogen escapes worldwide between 2000 and 2021: a scoping review
Bibliographic record
Abstract
Laboratory-acquired infections (LAIs) and accidental pathogen escape from laboratory settings (APELS) are major concerns for the community. A risk-based approach for pathogen research management within a standard biosafety management framework is recommended but is challenging due to reasons such as inconsistency in risk tolerance and perception. Here, we performed a scoping review using publicly available, peer-reviewed journal and media reports of LAIs and instances of APELS between 2000 and 2021. We identified LAIs in 309 individuals in 94 reports for 51 pathogens. Eight fatalities (2·6% of all LAIs) were caused by infection with Neisseria meningitidis (n=3, 37·5%), Yersinia pestis (n=2, 25%), Salmonella enterica serotype Typhimurium (S Typhimurium; n=1, 12·5%), or Ebola virus (n=1, 12·5%) or were due to bovine spongiform encephalopathy (n=1, 12·5%). The top five LAI pathogens were S Typhimurium (n=154, 49·8%), Salmonella enteritidis (n=21, 6·8%), vaccinia virus (n=13, 4·2%), Brucella spp (n=12, 3·9%), and Brucella melitensis (n=11, 3·6%). 16 APELS were reported, including those for Bacillus anthracis, SARS-CoV, and poliovirus (n=3 each, 18·8%); Brucella spp and foot and mouth disease virus (n=2 each, 12·5%); and variola virus, Burkholderia pseudomallei, and influenza virus H5N1 (n=1 each, 6·3%). Continual improvement in LAI and APELS management via their root cause analysis and thorough investigation of such incidents is essential to prevent future occurrences. The results are biased due to the reliance on publicly available information, which emphasises the need for formalised global LAIs and APELS reporting to better understand the frequency of and circumstances surrounding these incidents.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".