Differential COVID-19 infection rates in children, adults, and elderly: evidence from 38 pre-vaccination national seroprevalence studies
Bibliographic record
Abstract
ABSTRACT Background COVID-19 exhibits a steep age gradient of infection fatality rate. There has been debate about whether extra protection of elderly and other vulnerable individuals (precision shielding) is feasible, and, if so, to what extent. Methods We used systematically retrieved data from national seroprevalence studies conducted in the pre-vaccination era. Studies were identified through SeroTracker and PubMed searches (last update May 17, 2022). Studies were eligible if they targeted representative general populations without high risk of bias. Seroprevalence estimates were noted for children, non-elderly adults, and elderly adults, using cut-offs of 20, and 60 years (or as close to these ages, if they were not available). Results Thirty-eight national seroprevalence studies from 36 different countries were included in the analysis. 26/38 also included pediatric populations. 25/38 studies were from high-income countries. The median ratio of seroprevalence in the elderly versus non-elderly adults (or non-elderly in general, if pediatric and adult population data were not offered separately) was 0.90-0.95 in different analyses with large variability across studies. In 5 studies (all of them in high-income countries), there was significant protection of the elderly with ratio <0.40. The median was 0.83 in high-income countries and 1.02 in other countries. The median ratio of seroprevalence in children versus adults was 0.89 and only one study showed a significant ratio of <0.40. Conclusion Precision shielding of elderly community-dwelling populations before the availability of vaccines was feasible in some high-income countries, but most countries failed to achieve any substantial focused protection of this age group. summary 38 COVID-19 nationally representative seroprevalence studies conducted before vaccination campaigns were systematically identified. Median seroprevalence ratio in elderly versus non-elderly adults was 0.90-0.95, indicating no generally achieved precision shielding of elderly. In 5 studies, substantial protection (ratio <0.40) was observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".