Severity of SARS-CoV-2 reinfections in second wave determines likelihood of mild endemicity
Bibliographic record
Abstract
Abstract Immunity to SARS-CoV-2 is building up globally, but will this be sufficient to prevent future COVID-19 epidemics in the face of variants and waning immunity? Manaus, Brazil offers a concerning glimpse of what may come: six months after the majority of the city’s population experienced primary infection, a second wave with a new strain resulted in more deaths than the first wave. Current hypotheses for this surge rely on prior immunity waning due to time and antigenic distance. Here we show this hypothesis predicts a severe endemic state. We propose an alternative hypothesis in which individuals infected in the first wave lose protection against transmission but retain immunity against severe disease and show this hypothesis is equally compatible with existing data. In this scenario, the increased number of deaths is due to an increased infection fatality ratio (IFR) for primary infections with the new variant. This alternative predicts a mild endemic state will be reached within decades. Collecting data on the severity of reinfections and infections post-vaccination as a function of time and antigenic distance from the original exposure is crucial for optimizing control strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".