Short report: Introduction of chikungunya virus ECSA genotype into the Brazilian Midwest and its dispersion through the Americas
Bibliographic record
Abstract
Since introduction into Brazil in 2014, chikungunya virus (CHIKV) has presented sustained transmission, although much is unknown about its circulation in the midwestern states. Here, we analyze 24 novel partial and near complete CHIKV genomes from Cuiaba, an urban metropolis located in the Brazilian midwestern state of Mato Grosso (MT). Nanopore technology was used for sequencing CHIKV complete genomes. Phylogenetic and epidemiological approaches were used to explore the recent spatio-temporal evolution and spread of the CHIKV-ECSA genotype in Midwest Brazil as well as in the Americas. Epidemiological data revealed a reduction in the number of reported cases over 2018-2020, likely as a consequence of a gradual accumulation of herd-immunity. Phylogeographic reconstructions revealed that at least two independent introductions of the ECSA lineage occurred in MT from a dispersion event originating in the northeastern region and suggest that the midwestern Brazilian region appears to have acted as a source of virus transmission towards Paraguay, a bordering South American country. Our results show a complex dynamic of transmission between epidemic seasons and suggest a possible role of Brazil as a source for international dispersion of the CHIKV-ECSA genotype to other countries in the Americas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".