Evaluating the frequency of asymptomatic Ebola virus infection
Bibliographic record
Abstract
The potential for asymptomatic infection from Ebola viruses has long been questioned. Knowing the proportion of infections that are asymptomatic substantially changes the predictions made by mathematical models and alters the corresponding decisions based upon these models. To assess the degree of asymptomatic infection occurring during an Ebola virus disease (EVD) outbreak, we carried out a serological survey in the Djera district of the Equateur province of the Democratic Republic of the Congo affected by an Ebola outbreak in 2014. We sampled all asymptomatic residents ( n = 182) of 48 households where at least one case of EVD was detected. To control for potential background seroprevalence of Ebola antibodies in the population, we also sampled 188 individuals from 92 households in an unaffected area with a similar demographic background. We tested the sera collected for anti-Ebola IgG and IgM antibodies at four different dilutions. We then developed a mixture model to estimate the likely number of asymptomatic patients who developed IgM and IgG responses to Ebola antigens in both groups. While we detected an association between medium to high titres and age, we did not detect any evidence of increased asymptomatic infection in the individuals who resided in the same household as cases. This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".