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Record W2782311721 · doi:10.14745/ccdr.v44i01a04

Canada’s Pandemic Influenza Preparedness: Surveillance strategy

2018· article· en· W2782311721 on OpenAlexfundvenueaboutno aff
Benoît Henry

Bibliographic record

VenueCanada Communicable Disease Report · 2018
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsnot available
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsPreparednessPandemicDisease surveillanceMedical emergencyMedicineEnvironmental healthCoronavirus disease 2019 (COVID-19)Political scienceDiseasePublic healthInfectious disease (medical specialty)Nursing

Abstract

fetched live from OpenAlex

The Canadian Pandemic Influenza Preparedness: Planning Guidance for the Health Sector (CPIP) is a guidance document that outlines key health sector preparedness activities designed to ensure Canada is ready to respond to the next influenza pandemic. This article outlines Canada's pandemic influenza surveillance strategy as described in the CPIP Surveillance Annex. The strategy builds on the surveillance activities used for seasonal influenza and incorporates lessons learned from the 2009 H1N1 pandemic, including improved information sharing, improved electronic links among Federal/Provinical/Territorial (FPT) partners and improved surveillance for Indigenous communities. Key elements of the surveillance strategy include early detection and investigation of a novel influenza virus through the reporting of cases or clusters of severe acute respiratory infections and laboratory detections of novel influenza viruses. Community-based surveillance will provide information on clinical severity, age groups affected and risk factors associated with severe disease. Severe outcome surveillance will capture data on hospitalizations and deaths. Laboratory surveillance will include weekly reports of respiratory virus detections. The response activities are adaptable to the demands of different levels of pandemic activity and impact, supported by a set of triggers for the activation and deactivation. Surveillance will be linked with other response components, such as communications, research, assessment and evaluation. This is an evergreen document that will be updated regularly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.298
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes3
Has abstractyes

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