Transmission of Respiratory Syncytial Virus genotypes in Cali, Colombia
Bibliographic record
Abstract
Abstract Background Colombia's climatological variety, added to pathogen diversity, creates local niches for infectious diseases. In Bogotá, respiratory syncytial virus causes 30%‐52% of the cases of respiratory infections. In coastal or inter‐Andean cities with higher temperature and longer dry seasons, frequency of this virus is 7%‐13%. By 2017, increased hospitalizations due to airway infections occurred in regions whose weather is differently influenced by "El Niño Southern Oscillation" than in Bogotá, although microbial diversity might have also been involved. Methods For Cali, an inter‐Andean city with warm tropical weather, records of respiratory syncytial virus from 2014 to 2018, in children two years old or younger, were analyzed, and genotypes transmitted during 2016‐2017 were identified based on partial sequences of glycoprotein G. Results Most cases of respiratory syncytial virus in Cali occur in the first semesters, with peaks expressed around March‐April, without a clear association with pluviosity. Unlike the biannual rotating pattern of Bogotá, co‐circulation of types A and B was detected. As years pass, transmission seasons are becoming longer and frequencies of the virus augment. The viral genotypes identified follow international trends with dominance of Ontario and Buenos Aires clades. Similar to other isolates in these clades, viruses from Cali exhibit glycosylation variability that may account for their fitness. Conclusions The pattern of respiratory syncytial virus transmission in Cali differs from that in Bogotá. Its epidemiology is shifting and will remain so with the advent of novel respiratory diseases. This may impact the introduction of vaccination schemes for these or other respiratory viruses.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".