Viral aetiology of acute respiratory infections among children and associated meteorological factors in southern China
Bibliographic record
Abstract
BACKGROUND: Acute respiratory infections (ARIs) are common in children and mostly caused by viruses, but the significance of the detection of multiple viruses in ARIs is unclear. This study investigated 14 respiratory viruses in ARIs among children and associated meteorological factors in Shantou, southern China. METHODS: Paired nasal/throat-flocked swabs collected from 1,074 children with ARIs, who visited outpatient walk-in clinics in a tertiary hospital between December 2010 and November 2011, were examined for fourteen respiratory viruses--influenza viruses (FluA, FluB), respiratory syncytial viruses (RSV A and B), human coronaviruses (hCoV: 229E, OC43, HKU1, NL63), human metapneumoviruses (hMPV A and B), parainfluenza viruses (PIV1-4), human rhinoviruses (HRV A, B, C), enteroviruses (EV), adenoviruses (ADV), human bocavirus (hBoV), and human parechoviruses (hPeV)--by multiplex real-time PCR. RESULTS: We identified at least one virus in 82.3% (884/1,074) and multiple viruses in 38.6% (415/1,074) of patients. EV and HRV were the most frequently detected single viruses (42.3%, 374/884 and 39.9%, 353/884 respectively) and co-detected pair (23.1%, 96/415). Overlapping seasonal trends of viruses were recorded over the year, with dual peaks for EV and single peaks for the others. By logistic regression analysis, EV was positively associated with the average temperature and humidity, hCoV, and PIV4, but negatively with HRV, PIV3, and hBoV. HRV was inversely associated with EV and PIV3. CONCLUSIONS: This study reports high viral detection and co-detection rates in pediatric ARI cases mainly due to EV and HRV. Many viruses circulated throughout the year with similar seasonal trends in association with temperature, humidity, and wind velocity. Statistically significant associations were present among the viruses. Understanding the polyviral etiology and viral interactions in the cases with multiple viruses warrants further studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".