Estimating the effect of contact tracing during the early stage of an epidemic
Bibliographic record
Abstract
Contact tracing is an important public health measure to control disease transmission. However, it is difficult to assess contact tracing during the exponential stage of an epidemic with multiple control measures, because the exponential growth rate is influenced by all measures. We present new SEIR and SEAIR contact tracing models that track contacts in randomly mixed populations, and calibrate them to simulated epidemic curves to determine the data that allow us to assess the effect of contact tracing. We find that new-case counts, counts of cases identified by contact tracing (or voluntary tests), and counts of symptomatic onset are necessary to identify model parameters and evaluate the effect of contact tracing. We fit our contact tracing models to COVID-19 pandemic data in Ontario, Canada, during March 16-May 1, 2020. Our results show that approximately 29% of cases were identified via contact tracing of close contacts. Contact tracing moderately reduces the control reproduction number by about 25%, but significantly reduces the prevalence by more than half. Ignoring asymptomatic transmissions gives similar estimates for the effect of contact tracing, but significantly underestimates the prevalence.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".