Analysis of overdispersion in airborne transmission of Covid-19
Bibliographic record
Abstract
Abstract Superspreading events and overdispersion are hallmarks of the Covid-19 pandemic. To gain insight into the nature and controlling factors of these superspreading events and heterogeneity in transmission, we conducted mechanistic modeling of SARS-CoV-2 transmission by infectious aerosols using real-world occupancy data from a large number of full-service restaurants in ten US metropolises. Including a large number of factors that influence disease transmission in these settings, we demonstrate the emergence of a stretched tail in the probability density function of secondary infection numbers indicating strong heterogeneity in individual infectivity. Derived analytical results further demonstrate that variability in viral loads and variability in occupancy, together, lead to overdispersion in the number of secondary infections arising from individual index cases. Our analysis, connecting mechanistic understanding of SARS-CoV-2 transmission by aerosols with observed large-scale epidemiological characteristics of Covid-19 outbreaks, adds an important dimension to the mounting body of evidence with regards to the determinants of airborne transmission of SARS-CoV-2 by aerosols in indoor settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".