Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar
Bibliographic record
Abstract
Detecting and quantifying changes in the growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers' rationale for implementing or continuing interventions aimed at reducing their impact. Substantial changes in SARS-CoV-2 prevalence with the emergence of variants have provided an opportunity to investigate different methods for doing this. We collected polymerase chain reaction (PCR) results from all participants in the United Kingdom's COVID-19 Infection Survey between August 1, 2020, and June 30, 2022. Change points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalized additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8 799 079 study visits, 147 278 (1.7%) were PCR-positive. Change points associated with the emergence of major variants were estimated to occur a median of 4 days earlier (IQR, 0-8) when using GAMs versus ISR. When estimating recent change points using successive data periods, 4 change points (4/96) identified by GAMs were not found when adding later data or by ISR. Change points were detected 3-5 weeks after they occurred under both methods but could be detected earlier within specific subgroups. Change points in growth rates of SARS-CoV-2 can be detected in near real time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories, both methods could be used in parallel.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".