Alternative Methods of Estimating an Incubation Distribution
Bibliographic record
Abstract
BACKGROUND: Accurate and precise estimates of the incubation distribution of novel, emerging infectious diseases are vital to inform public health policy and to parameterize mathematical models. METHODS: We discuss and compare different methods of estimating the incubation distribution allowing for interval censoring of exposures, using data from the severe acute respiratory syndrome (SARS) epidemic in 2003 as an example. RESULTS: Combining data on unselected samples of 149 and 168 patients with defined exposure intervals from Toronto and Hong Kong, respectively, we estimated the mean and variance of the incubation period to be 5.1 day and 18.3 days and the 95th percentile to be 12.9 days. We conducted multiple linear regression on the log incubation times and found that incubation was significantly longer in Toronto than in Hong Kong and in older compared with younger patients, while it was significantly shorter in healthcare workers than in other patients. CONCLUSIONS: Our findings suggest subtle but important heterogeneities in the incubation period of SARS among different strata of patients. Robust estimation of the incubation period should be independently carried out in different settings and subgroups for novel human pathogens using valid statistical methods.
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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 | 0.045 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".