The virus evolves: four public health priorities for reducing the evolutionary potential of SARS-CoV-2
Bibliographic record
Abstract
As scientists who study evolution, we are concerned about the threat posed to public health by evolving SARS-CoV-2 variants. We celebrate the start of vaccination campaigns, enabled by remarkable scientific achievements, but these advances are now at risk of being undermined by evolution. In particular, we emphasize that—unless we keep case numbers low—novel variants will continue to emerge, increasing the chances that some will evade vaccine-induced immunity. These dangerous consequences of SARS-CoV-2 evolution are looming, but rapid viral evolution is not inevitable. Fundamental principles of evolution provide clear guidelines for slowing down the evolutionary process. With these principles in mind and echoing the calls made by others (see the supplementary material for complete references and signatories), we urge policymakers to prioritize the following: One of the most well established truths of evolution is that adaptive change is more likely when populations are larger. This means that a drastic reduction in infections will not only reduce illness and save lives in the face of variants that are already present, but it will guard against the conditions that favor the evolution of new variants of concern, which may be more transmissible, more virulent, or more able to escape immunity (“escape variants”). The emergence and spread of escape variants is favored by transmission of the virus to and from people who are immunized (whether they have been immunized by natural infection or by vaccines). Although such transmission chains may be infrequent, they become more likely when case numbers are high in areas where vaccinations are being rolled out. Therefore, in order to limit the emergence and spread of escape variants as vaccination campaigns proceed, policy-makers should encourage vaccinated people to continue to adhere to mitigation measures (e.g., masks, physical distance, ventilation) while case numbers remain high. Genomic surveillance (regular sequencing of a representative sample of cases) is essential for identifying the emergence of new variants. Sharing these sequence data on public repositories as quickly as possible will enable rapid responses when new variants of concern are detected. Moreover, genomic data should be paired with patient metadata so that any variants that alter the disease characteristics can be identified. Concurrent with genomic surveillance, increased genotyping of samples from cases for known variants of concern (e.g., via RT-PCR, polymerase chain reaction, which can return test results very quickly) is needed for rapid detection so that targeted control efforts can be put in place around those cases. We all live on the same planet. Leaving the epidemic uncontrolled anywhere leaves the global population vulnerable to the evolution of variants that can escape immunity. Therefore, we support the scientific consensus calling for global coordination on both vaccination campaigns and genomic surveillance. We ask leaders to seize this window of opportunity; protect the health of citizens and economies by taking swift action to guard against the threat of ongoing viral evolution. Supplemental file with a list of society signatories and full references is available at BIOSCI online. Joint Public Policy Committee: The Society for the Study of Evolution and The American Society of Naturalists Co-signing scientific societies include the Society for the Study of Evolution, the American Society of Naturalists, the European Society for Evolutionary Biology, the American Institute for Biological Sciences, the Canadian Society for Ecology and Evolution, the Society of Systematic Biologists, the Indian Society of Evolutionary Biologists, the International Society for Behavioral Ecology, the Netherlands Society for Evolutionary Biology, the Chilean Society of Evolution, the European Society for Evolution and Development, and the Konrad Lorenz Institute for Evolution and Cognition Research.
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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 | 0.036 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".