Seroprevalence of Alkhurma and Other Hemorrhagic Fever Viruses, Saudi Arabia
Bibliographic record
Abstract
A 2009 deployment of military units from several Saudi Arabian provinces to Jazan Province, Saudi Arabia, enabled us to evaluate exposure to Alkhurma, Crimean-Congo, dengue, and Rift Valley hemorrhagic fever viruses.Seroprevalence to all viruses was low; however, Alkhurma virus seroprevalence was higher (1.3%) and less geographically restricted than previously thought.J azan is a Red Sea port city on Saudi Arabia's southern border with Yemen and the capital of Jazan Province.The region serves as an east-west portal from sub-Saharan Africa at Djibouti and a south-north route across the Yemeni frontier.It is a heavily traveled corridor for humans and animals entering Saudi Arabia, particularly during the annual Hajj pilgrimage.Malaria is endemic, and arbovirus infections are well described, most notably a 2000-2001 Rift Valley fever (RVF) outbreak on the Saudi Arabia-Yemen frontier.More than 880 confi rmed RVF cases were reported.This outbreak was also notable for a case-fatality rate of 14% (1).Sporadic infections caused by Crimean-Congo hemorrhagic fever virus (CCHFV) occur as well.The virus is endemic across a wide geographic range, from the Middle East to Africa and central Asia (2).Dengue virus (DENV), a pathogen well known for large outbreaks and global circulation, also causes seasonal outbreaks in Saudi Arabia's western provinces; most outbreaks occur farther north in the urban centers of Jeddah and Makkah (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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".