Yellow fever epizootics in non-human primates, Southeast and Northeast Brazil (2017 and 2018)
Bibliographic record
Abstract
BACKGROUND: Yellow fever (YF) is a severe, infectious, but non-communicable arboviral hemorrhagic disease. In the last decades, yellow fever virus (YFV) infections have been prevalent in endemic areas in Brazil, affecting human and non-human primate (NHP) populations. Monitoring of NHP infection started in 1999, and reports of epizootic diseases are considered important indicators of viral transmission, particularly in relation to the sylvatic cycle. This study presents the monitoring of YFV by real-time RT-PCR and the epidemiological findings related to the deaths of NHPs in the south-eastern states and in the north-eastern state of Bahia, during the outbreak of YF in Brazil during 2017 and 2018. METHODS: A total of 4198 samples from 2099 NHPs from south-eastern and north-eastern Brazilian states were analyzed by real-time reverse transcription polymerase chain reaction (rtRT-PCR). RESULTS: A total of 4198 samples from 2099 NHPs from south-eastern and north-eastern Brazilian states were collected between 2017 and 2018. The samples were subjected to molecular diagnostics for YFV detection using real-time reverse transcription polymerase chain reaction (rtRT-PCR) techniques. Epizootics were coincident with human YF cases. Furthermore, our results showed that the YF frequency was higher among marmosets (Callithrix sp.) than in previous reports. Viremia in species of the genus Alouatta and Callithrix differed greatly. DISCUSSION: Our results indicate a need for further investigation of the role of Callithrix spp. in the transmission cycles of YFV in Brazil. In particular, YFV transmission was observed in a region where viral circulation has not been recorded for decades and thus vaccination has not been previously recommended. CONCLUSIONS: This highlights the need to straighten epizootic surveillance and evaluate the extent of vaccination programmes in Brazil in previously considered "YFV-free" areas of the country.
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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 | 0.001 | 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".