Clinical Laboratory Response to a Mock Outbreak of Invasive Bacterial Infections: a Preparedness Study
Bibliographic record
Abstract
Large hospital-based clinical laboratories must be prepared to rapidly investigate potential infectious disease outbreaks. To challenge the ability of our molecular diagnostics laboratory to use whole-genome sequencing in a potential outbreak scenario and identify impediments to these efforts, we studied 84 invasive serotype emm59 group A streptococcus (GAS) strains collected in the United States. We performed a rapid-response exercise to the mock outbreak scenario using whole-genome sequencing, genome-wide transcript analysis, and mouse virulence studies. The protocol changes installed in response to the lessons learned were tested in a second iteration. The initial investigation was completed in 9 days. Whole-genome sequencing showed that the invasive infections were caused by multiple subclones of epidemic emm59 GAS strains likely spread to the United States from Canada. The phylogenetic tree showed a strong temporal-spatial structure with diversity in mobile genetic element content, features that are useful for identifying closely related strains and possible transmission events. The genome data informed the epidemiology, identifying multiple patients who likely acquired the organisms through direct person-to-person transmission. Transcriptome analysis unexpectedly revealed significantly altered expression of genes encoding a two-component regulator and the hyaluronic acid capsule virulence factor. Mouse infection studies confirmed a high-virulence capacity of these emm59 organisms. Whole-genome sequencing, coupled with transcriptome analysis and animal virulence studies, can be rapidly performed in a clinical environment to effectively contribute to patient care decisions and public health maneuvers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".