Epidemiology of parainfluenza infection in England and Wales, 1998–2013: any evidence of change?
Bibliographic record
Abstract
Human parainfluenza virus (HPIV) infections are one of the commonest causes of upper and lower respiratory tract infections. In order to determine if there have been any recent changes in HPIV epidemiology in England and Wales, laboratory surveillance data between 1998 and 2013 were analysed. The UK national laboratory surveillance database, LabBase, and the newly established laboratory-based virological surveillance system, the Respiratory DataMart System (RDMS), were used. Descriptive analysis was performed to examine the distribution of cases by year, age, sex and serotype, and to examine the overall temporal trend using the χ 2 test. A random-effects model was also employed to model the number of cases. Sixty-eight per cent of all HPIV detections were due to HPIV type 3 (HPIV-3). HPIV-3 infections were detected all year round but peaked annually between March and June. HPIV-1 and HPIV-2 circulated at lower levels accounting for 20% and 8%, respectively, peaking during the last quarter of the year with a biennial cycle. HPIV-4 was detected in smaller numbers, accounting for only 4% and also mainly observed in the last quarter of the year. However, in recent years, HPIV-4 detection has been reported much more commonly with an increase from 0% in 1998 to 3·7% in 2013. Although an overall higher proportion of HPIV infection was reported in infants (43·0%), a long-term decreasing trend in proportion in infants was observed. An increase was also observed in older age groups. Continuous surveillance will be important in tracking any future changes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".