Anticipating visceral leishmaniasis epidemics due to the conflict in Northern Ethiopia
Bibliographic record
Abstract
On November 4, 2020, conflict erupted between the Ethiopian federal government and the Northern Ethiopian regional forces called the Tigray People's Liberation Front [1,2].The crisis has since driven over 4 million people to flee their homes, often with little possessions and no shelter, remaining internally displaced in the Tigray, Afar, and Amhara regions known to be endemic for visceral leishmaniasis (VL) [1][2][3][4].The Internal Displacement Monitoring Centre estimates that this conflict triggered the "highest displacement figures ever recorded for any country in any given year" [2].An additional 59,000 people have sought safety in eastern Sudan, concentrated around Gedaref and southern Kassala states, also known to be highly endemic for VL [4,5].East Africa currently composes the global epicenter of VL, accounting for 66% of the reported cases worldwide [6].It also comprises the area with the highest rate of VL-HIV coinfection, further amplifying VL transmission [4,7].Considering that conflict has previously triggered large VL epidemics with elevated case fatality, additional funding and operational coordination for a regional approach to VL screening, treatment and prevention will be required for the WHO to reach its goal of eliminating this neglected tropical disease as a public health problem by 2030 [4,5,8,9].Visceral leishmaniasis is a vector-borne disease transmitted by sandflies [3,4].This systemic disease affects the reticuloendothelial system and is fatal if untreated [4].In East Africa, visceral leishmaniasis is caused by Leishmania donovani and is predominantly transmitted by the Phlebotomus orientalis sandflies that thrive in the Acacia-Balanites forests along Ethiopia's northwestern border with Sudan [3].Phlebotomus orientalis is exophagic, preferring to bite the human host outside the home [10].Thus, sleeping outdoors is a significant risk factor for VL, a factor that renders refugees without shelter particularly vulnerable to VL acquisition [9,10].Since VL in East Africa is principally understood to be anthroponotic, human migration acts as one of the primary drivers of disease transmission [4,9].Previous refugee crises have produced large deadly epidemics of VL in East Africa [5,8].Within a year of conflict breaking out in South Sudan in 2013, cases of VL more than tripled in the Jonglei and Upper Nile States [5].Earlier wars catalyzed devastating VL epidemics.After war exploded in South Sudan in 1983, approximately one third of the Western Upper Nile's population of 280,000 died of VL during a 10-year period [8].While conflict has long been known to trigger outbreaks of neglected tropical diseases, epidemics of VL are deemed to be the deadliest as conflict interferes with the provision of care for a fatal disease [5].Furthermore, human migration introduces VL to new locations outside those of known endemicity.This phenomenon is well documented among Ethiopian migrant laborers who descend from high-altitude Amhara areas to work on commercial farms on the Sudanese border [3].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".