Ebola virus outbreak in North Kivu and Ituri provinces, Democratic Republic of Congo, and the potential for further transmission through commercial air travel
Bibliographic record
Abstract
BACKGROUND: The 2018-2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces, Democratic Republic of Congo (DRC), continues to spread. The recent discovery of cases in Uganda and in Goma, a major city in the eastern DRC, raises concern for potential EVD transmission in distant locales via commercial air travel. METHODS: We examined air travel patterns from the affected region with itinerary-level data from the International Air Transport Association for the year 2018 between July and October, inclusive. We focused on three scenarios: (i) travel from Beni airport, (ii) travel from Beni, Goma and Bunia airports and (iii) travel from Beni, Goma and Bunia, and Kigali airports. We evaluated country-level Infectious Disease Vulnerability Index (IDVI) scores for traveller destinations. RESULTS: There were 2255 commercial air passengers departing from Beni Airport during the specified time frame, all with domestic destinations, and 55% of which were to Goma. A total of 29 777 passengers travelled from Beni, Bunia and Goma airports during this time frame, with most travel (94.6%) departing from Goma Airport. A total of 72.4% of passengers' final destination from these three airports were within the DRC, primarily to Kinshasa. There were 166 281 outbound passengers from Beni, Bunia, Goma and Kigali airports with the majority (82.1%) of passengers departing from Kigali. The most frequent destinations from these airports were Nairobi, Kinshasa and Entebbe. Eight of the 10 destinations with greatest passenger volumes are to countries with IDVI scores less than 0.4. CONCLUSION: There is little commercial airline connectivity from the current EVD-affected area; however, larger cities in DRC and throughout East Africa should be aware of the low potential for EVD importation through this route. Most countries at greatest risk for EVD importation have limited capacity to manage these cases.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".