Why has the Ebola outbreak in West Africa been so challenging to control?
Bibliographic record
Abstract
West Africa is in the midst of the largest Ebola outbreak ever; there have been over 1000 deaths and many new cases are reported each day. The World Health Organization (WHO) declared it an outbreak in March 2014 and on August 6, 2014 the WHO declared the outbreak a public health emergency of international concern. Based on the number of deaths and total number of cases reported to the WHO as of August 11, 2014, the current outbreak has an overall mortality rate of 55%. Outbreak control measures against Ebola virus disease are effective. Why then, has this outbreak been so challenging to control? Ebola is transmitted through bodily fluids and immediately attacks the immune system, then progressively attacks the major organs and the lining of blood vessels. Sierra Leone, Guinea and Liberia are small countries that have limited resources to respond to prolonged outbreaks, especially in rural areas. This has been made more challenging by the fact that health care workers are at risk of contracting Ebola virus disease. Treatment to date has been supportive, not curative and outbreak control strategies have been met with distrust due to fear and misinformation. However, important progress is being made. The international response to Ebola is gaining momentum, communication strategies have been developed to address the fear and mistrust, and promising treatments are under development, including a combination of three monoclonal antibodies that has been administered to two American Ebola infected health care workers. The National Microbiology Laboratory of the Public Health Agency of Canada (PHAC) has been supporting laboratory diagnostic efforts in West Africa and PHAC has been working with the provinces and territories and key stakeholders to ensure Canada is prepared for a potential Ebola importation.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".