Human respiratory syncytial virus and influenza seasonality patterns—Early findings from the WHO global respiratory syncytial virus surveillance
Bibliographic record
Abstract
BACKGROUND: Human respiratory syncytial virus (RSV) causes illnesses among all age groups and presents a burden to healthcare services. To better understand the epidemiology and seasonality of RSV in different geographical areas, the World Health Organization (WHO) coordinated a pilot initiative to access the feasibility of establishing RSV surveillance using the existing Global Influenza Surveillance and Response System (GISRS) platform. OBJECTIVES: To describe and compare RSV and influenza seasonality in countries in the northern andsouthern temperate, and tropics during the period January 2017 to April 2019. METHODS: Fourteen countries in six WHO regions participating in the GISRS were invited for the pilot. Hospitalized patients presenting with severe acute respiratory illness (SARI), SARI without fever and outpatients presenting with acute respiratory illness (ARI) were enrolled from January 2017 to April 2019. The expected minimum sample size was 20 samples per week, year-round, per country. Real-time RT-PCR was used to detect RSV and influenza viruses. Results were uploaded to the WHO FluMart platform. RESULTS: Annual seasonality of RSV was observed in all countries, which overlapped to a large extent with the influenza activity. In countries, in temperate regions RSV peaked in the autumn/winter months. In Egypt, a subtropical country, RSV activity peaked in the cooler season. In the tropical regions, RSV peaked during the rainy seasons. CONCLUSION: Early findings from the WHO RSV surveillance pilot based on the GISRS suggest annual seasonal patterns for of RSV circulation that overlap with influenza. RSV surveillance needs to be continued for several more seasons to establish seasonality patterns to inform prevention and control strategies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".