Genomics Research: The Underpinning of Infectious Disease Prevention and Control Strategies
Bibliographic record
Abstract
Despite the availability of antibiotics and vaccines, infectious diseases remain the second leading cause of death worldwide. The dynamic nature of infectious diseases due to persistent, emerging, and re-emerging infections continues to challenge health care systems around the world. As such, there is an urgent need for improvements in guidelines and practices that is only achievable through evidence-informed applied public health research. But what direction should this research take? A major weakness in current prevention and control strategies stems from an antiquated ‘one-size-fits-all' paradigm that fails to sufficiently recognize inherent differences in both host and pathogen. Indeed, individuals do not respond equally to infection, and pathogens of the same species are more disparate that once thought. It is incontrovertible that much of this diversity is attributable to genetic variation. Over the past two decades, genomics has provided remarkable insight into susceptibility, resistance, and progression of infection, yet the gap between genomics research and public health application remains large. Leaders in public health research, such as the Public Health Agency of Canada (PHAC) and the Centers for Disease Control (CDC), recognize that the knowledge garnered from genome-based research can be applied to prevent adverse outcomes of infection. As such, these agencies actively engage with major academic institutions to translate genome-based research into socially, legally, and ethically acceptable public health application. This Special Issue highlights the role of genomics in advancing our understanding of host-pathogen interactions and in improving the quality of mainstay public health tools including genomic epidemiology, diagnostics/screening, and vaccines.I sincerely thank the authors for their collective efforts in making this Special Issue possible. Ross Duncan (Laboratory for Foodborne Zoonosis, PHAC) and Dr. Bartha Knoppers (McGill University) conceived of the idea for this Issue and graciously invited me to serve as Guest Editor. Finally, I thank Dr. Tom Wong (Centre for Communicable Diseases and Infection Control, PHAC) for his critical comments and PHG Editor Dr. Elena Ambrosino for her guidance and support throughout the making of this Special Issue.Suneil Malik, PhDGuest EditorPublic Health Agency of Canada,Laboratory for Foodborne ZoonosisOffice of Biotechnology, Genomics and Population HealthOttawa, Ontario, Canada
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".