50 Ebola epidemic in war-torn Eastern Democratic Republic of Congo 2018: Rapid assessment of knowledge, attitudes, and practices
Bibliographic record
Abstract
The 2014–2016 West Africa Ebola outbreak claimed over 11,000 lives. Misconceptions and high risk behaviours such as unsafe burial practices contributed to the spread of the virus. Another Ebola epidemic is currently spreading in Eastern DRC, a densely populated, active conflict zone with many internally displaced persons (IDPs). The current outbreak is also significant in its high proportion of pediatric cases in recent months, presenting new challenges in the management of this crisis. Local data on the knowledge, attitudes, and practices from the area could inform public health messaging during the current unprecedented epidemic. Our objective was to assess local community members’ perceptions related to the current Ebola outbreak. We surveyed 582 community members and conducted focus group discussions with key community informants from 4 to 17 August, 2018 in the municipalities of Mangina (outbreak epicenter), Béni, Butembo, and Komando (IDP camp) in Eastern DRC. Knowledge of Ebola transmission was high (>80% correctly identified body fluids and contact with a corpse as risk factors), as was affective response (90% “worried” about Ebola). Nonetheless, a small but significant minority expressed attitudes that could hamper control efforts: unwillingness to bring suspected family members to an Ebola treatment unit (17%), intention to hide sick family members from authorities (17%), intention to touch or wash the body of a family member who died of suspected Ebola (8%), and unwillingness to accept a trained burial team (10%). Qualitative data corroborated these findings, emphasizing strongly held cultural preferences to be near the dying and deceased, early eyewitness reports of social resistance to control efforts, and accounts of substantial impediments posed by armed militias. On the other hand, acceptance of the new vaccine was high (80% would accept vaccination for their family). Community engagement for safe and dignified burials and attention to social resistance to control efforts may be as important in Eastern DRC as they were in West Africa in 2014–2016. The new vaccine, if made available to the general public, would likely be widely accepted.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".