The Neglected Role of the Military as a Disease Vector: Implications for COVID-19 and for Global Public Health Policy
Bibliographic record
Abstract
With the development of communication and transportation technologies, increases in international trade, and mass population movements, chances of human-to-human transmission of infectious disease agents have increased, alongside the pressing need to understand their transmission mechanisms and develop effective responses to contain their spread. Since the onset of the Covid-19 pandemic, mass testing, contact tracing, isolation of confirmed cases and varying degrees of restriction on population movements have contributed to flattening the global disease curve. However, the role of military transmission in the spread of Covid-19 has been largely overlooked, not only by the military itself, but also by government officials, policymakers, and even medical professionals, despite the rich body of literature spanning at least a century providing evidence for the role of the military as a pathogen transmitter. We call attention to this omission, offer a snapshot of the historical evidence for military-civilian transmission of infectious disease and its disproportionate impact on vulnerable populations, and underscore the need to acknowledge the neglected role of the military as a disease vector for the successful design and implementation of a more equitable Covid-19 public health policy.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".