COVID-19 Case Age Distribution: Correction for Differential Testing by Age
Bibliographic record
Abstract
Abstract Background SARS-CoV-2 is a novel pathogen and is currently the cause of a global pandemic. Despite expected universal susceptibility to a novel pathogen, the pandemic to date has been characterized by higher observed incidence in the oldest individuals and lower incidence in children and adolescents. Differential testing by age group may explain some of these observed differences, but datasets linking case counts to public health testing volumes are uncommon. Methods We used data from Ontario, Canada. Case data were obtained from Ontario’s provincial line, while testing data were obtained from an information system with complete SARS-CoV-2 testing data for public, hospital, and private laboratories. Demographic and temporal patterns in reported case incidence, testing rates, and test positivity were explored using negative binomial regression models. Standardized morbidity and testing ratios (SMR, STR), and standardized test positivity (STP) were calculated by dividing age- and sex-specific rates by overall rates; demographic and temporal patterns in standardized ratios were explored using meta-regression. Testing adjusted SMR were estimated using linear regression models. Results Observed disease incidence and testing rates were highest in oldest individuals and markedly lower in those aged < 20. Temporal trends in disease incidence and testing were observed, but standardizing morbidity and testing ratios eliminated temporal trends (i.e., relative patterns by age and sex remained identical regardless of epidemic phase). After adjustment for testing frequency, SMR were lowest in children and adults aged 70 and older, approximately the same in adolescents as in the population as a whole and elevated in young adults (aged 20-29 years), providing a markedly different picture of the epidemic than seen with crude SMR or case-based incidence. Test-adjusted SMR were validated using seroprevalence data (Pearson correlation coefficient 0.82, P = 0.04). Conclusions Surveillance for SARS-CoV-2 infection is typically performed using only test-positive case data, without adjustment for testing frequency. Older adults are tested more frequently, likely due to increased disease severity, while children are under-tested. Adjustment for testing frequency results in a very different picture of SARS-CoV-2 infection risk by age, one that is consistent with estimates obtained through serological testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".