Epidemiological aspects of the Oropouche virus (Orthobunyavirus) in South America: A systematic review
Bibliographic record
Abstract
Introduction: The Oropouche virus (OROV) is an arbovirus that belongs to the genus Orthobunyavirus and family Peribunyaviridae, responsible for causing Oropouche fever (OF) in humans. The clinical diagnosis of is doubtful due to the non-specificity of the symptoms, which can lead to a mistaken diagnosis of other arboviruses. Thus, the survey of epidemiological data on the occurrence of has been a major challenge for public health authorities, especially in of South America. Aim: To determine the general exposure rate of OROV in Brazil and other countries in South America by a systematic review. An article search was carried out in the Pubmed/ Medline, Scopus, Cochrane, Lilacs, Electronic Scientific Online Library (SciELO) and Virtual Health Library (VHL) databases. Results:18 studies were selected as eligible to compose this review on epidemiological aspects of OROV. The studies were published from 1989 to 2020. Most studies were carried out in Brazil (12/18; 66.66%) and Peru (5/18; 27.77%), only one study collected samples from Peru, Ecuador, Bolivia and Paraguay. The test for the OROV was realized mainly by serological analysis. Of the 8005 samples analyzed, 1570 tested positive for the presence of OROV thus accounting a general exposure rate in South America of 19.61%. Brazil was responsible for more than half of the cases of OROV identified in South America (855/1570; 54.46%), however Peru has the highest rate of exposure to the virus (23.43% of frequency in Peru vs. 16.77% of frequency in Brazil). Conclusion: OROV stands out as an important public health problem in Amazonian countries in South America.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".