The domestication of SARS-CoV-2 into a seasonal infection by viral variants
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic was caused by the zoonotic betacoronavirus SARS-CoV-2. SARS-CoV-2 variants have emerged due to adaptation in humans, shifting SARS-CoV-2 towards an endemic seasonal virus. We have termed this process 'virus domestication'. Methods: We analyzed aggregate COVID-19 data from a publicly funded healthcare system in Canada from March 7, 2020 to November 21, 2022. We graphed surrogate calculations of COVID-19 disease severity and SARS-CoV-2 variant plaque sizes in tissue culture. Results and Discussion: Mutations in SARS-CoV-2 adapt the virus to better infect humans and evade the host immune response, resulting in the emergence of variants with altered pathogenicity. We observed a decrease in COVID-19 disease severity surrogates after the arrival of the Delta variant, coinciding with significantly smaller plaque sizes. Overall, we suggest that SARS-CoV-2 has become more infectious and less virulent through viral domestication. Our findings highlight the importance of SARS-CoV-2 vaccination and help inform public policy on the highest probability outcomes during viral pandemics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".