The Global Influenza Hospital Surveillance Network: A Multicountry Public Health Collaboration
Bibliographic record
Abstract
Respiratory viruses represent a significant public health threat. There is the need for robust and coordinated surveillance to guide global health responses. Established in 2012, the Global Influenza Hospital Surveillance Network (GIHSN) addresses this need by collecting clinical and virological data on persons with acute respiratory illnesses across a network of hospitals worldwide. GIHSN utilizes a standardized patient enrolment and data collection protocol across its study sites. It leverages pre-existing national infrastructures and expert collaborations to facilitate comprehensive data collection. This includes demographic, clinical, epidemiological, and virologic data, and whole genome sequencing (WGS) for a subset of viruses. Sequencing data are shared in the Global Initiative on Sharing All Influenza Data (GISAID). GIHSN uses financing and governance approaches centered around public-private partnerships. Over time, GIHSN has included more than 100 hospitals across 27 countries and enrolled more than 168,000 hospitalized patients, identifying 27,562 cases of influenza and 44,629 of other respiratory viruses. GIHSN has expanded beyond influenza to include other respiratory viruses, particularly since the COVID-19 pandemic. In November 2023, GIHSN strengthened its global impact through a memorandum of understanding with the World Health Organization, aimed at enhancing collaborative efforts and data sharing for improved health responses. GIHSN exemplifies the value of integrating scientific research with public health initiatives through global collaboration and public-private partnerships governance. Future efforts should enhance the scalability of such models and ensure their sustainability through continued public and private support.
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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 | 0.066 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".