Fighting a fire versus waiting for a wave: Useful and not-so-useful analogies in times of SARS-CoV-2
Bibliographic record
Abstract
As SARS-CoV-2 has swept the planet, intermittent “lockdowns” have become a regular feature to control transmission. References to so-called recurring “waves” of infections remain pervasive among news headlines, political messaging, and public health sources. Here, we consider the power of analogies as a tool for facilitating effective understanding of biological processes by reviewing the successes and limitations of various analogies in the context of the COVID-19 pandemic. We also consider how, when analogies fall short, their ability to persuade can mislead public opinion and behaviour, even if unintentionally. While waves can be effective in conveying patterns of disease outbreak retrospectively, we suggest that process-based analogies might be more effective communication tools, given that they are easily mapped to underlying epidemiological concepts and can be extended to include more complex (e.g., spatial) dynamics. Though no single analogy perfectly captures disease dynamics, fire is particularly suitable for visualizing the epidemiological models that are used to understand disease trajectories, underscoring the importance of and reasoning behind control strategies, and, above all, conveying a sense of urgency to galvanise collective action. Note: This is the submitted version (prior to peer review) of an article that has now been published in BioScience following peer review. The version of record Archer et al (2022) is available online at: 10.1093/biosci/biac017
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".