Post-death Transmission of Ebola: Challenges for Inference and\n Opportunities for Control
Bibliographic record
Abstract
Multiple epidemiological models have been proposed to predict the spread of\nEbola in West Africa. These models include consideration of counter-measures\nmeant to slow and, eventually, stop the spread of the disease. Here, we examine\none component of Ebola dynamics that is of growing concern -- the transmission\nof Ebola from the dead to the living. We do so by applying the toolkit of\nmathematical epidemiology to analyze the consequences of post-death\ntransmission. We show that underlying disease parameters cannot be inferred\nwith confidence from early-stage incidence data (that is, they are not\n"identifiable") because different parameter combinations can produce virtually\nthe same epidemic trajectory. Despite this identifiability problem, we find\nrobustly that inferences that don't account for post-death transmission tend to\nunderestimate the basic reproductive number -- thus, given the observed rate of\nepidemic growth, larger amounts of post-death transmission imply larger\nreproductive numbers. From a control perspective, we explain how improvements\nin reducing post-death transmission of Ebola may reduce the overall epidemic\nspread and scope substantially. Increased attention to the proportion of\npost-death transmission has the potential to aid both in projecting the course\nof the epidemic and in evaluating a portfolio of control strategies.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".