Influenza serological studies to inform public health action: best practices to optimise timing, quality and reporting
Bibliographic record
Abstract
BACKGROUND: Serological studies can detect infection with a novel influenza virus in the absence of symptoms or positive virology, providing useful information on infection that goes beyond the estimates from epidemiological, clinical and virological data. During the 2009 A(H1N1) pandemic, an impressive number of detailed serological studies were performed, yet the majority of serological data were available only after the first wave of infection. This limited the ability to estimate the transmissibility and severity of this novel infection, and the variability in methodology and reporting limited the ability to compare and combine the serological data. OBJECTIVES: To identify best practices for conduct and standardisation of serological studies on outbreak and pandemic influenza to inform public policy. METHODS/SETTING: An international meeting was held in February 2011 in Ottawa, Canada, to foster the consensus for greater standardisation of influenza serological studies. RESULTS: Best practices for serological investigations of influenza epidemiology include the following: classification of studies as pre-pandemic, outbreak, pandemic or inter-pandemic with a clearly identified objective; use of international serum standards for laboratory assays; cohort and cross-sectional study designs with common standards for data collection; use of serum banks to improve sampling capacity; and potential for linkage of serological, clinical and epidemiological data. Advance planning for outbreak studies would enable a rapid and coordinated response; inclusion of serological studies in pandemic plans should be considered. CONCLUSIONS: Optimising the quality, comparability and combinability of influenza serological studies will provide important data upon emergence of a novel or variant influenza virus to inform public health action.
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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 | 0.772 | 0.754 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.014 | 0.018 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".