Crimean–Congo Hemorrhagic Fever Virus Survey in Humans, Ticks, and Livestock in Agnam (Northeastern Senegal) from February 2021 to March 2022
Bibliographic record
Abstract
Crimean–Congo hemorrhagic fever virus (CCHFV) is widespread in Asia, Europe, and Africa. In Senegal, sporadic cases of CCHFV have been reported since 1960. Bordering Mauritania in northeastern Senegal, Agnam is an arid area in the region of Matam where CCHFV is endemic, which harbors a pastoralist community. Given the drought conditions of Agnam, inhabitants are in constant movement with their animals in search of pasture, which brings them into contact with pathogens such as arboviruses. To identify CCHFV in this area, we established a One Health site in order to analyze animal livestock, ticks and human samples collected over a one-year period by qRT-PCR and ELISA. Our analysis showed one (1/364) patient carried anti-CCHFV IgM and thirty-seven carried anti-CCHFV IgG (37/364). In livestock, anti-CCHFV IgG was detected in 13 (38.24%) of 34 sentinel sheep. The risk of CCHFV infection increased significatively with age in humans (p-value = 0.00117) and sheep (p-value = 1.18 × 10−11). Additional risk factors for CCHFV infection in sheep were dry seasons (p-value = 0.004) and time of exposure (p-value = 0.007). Furthermore, we detected a total of three samples with CCHFV RNA within Rhipicephalus evertsi evertsi and Rhipicephalus guilhoni tick species. Our results highlighted the usefulness of a One Health survey of CCHFV in pastoral communities at risk of arboviruses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".