Augmenting surveillance to minimize the burden of norovirus-like illness in Ontario: using telehealth data to detect the onset of community activity
Bibliographic record
Abstract
This thesis sets out to describe the essential elements required in the creation of a syndromic surveillance system for human infectious disease. Using this information, this thesis also presents a novel early warning syndromic surveillance system for the winter norovirus season in the province of Ontario, Canada. Telehealth data were utilized as the source of syndromic data alongside laboratory data to confirm the onset of the winter norovirus season. Syndromic methods were selected for this novel surveillance system because they can predict disease outbreaks earlier than laboratory and other traditional surveillance methods and cover a wider scope of the population. Norovirus outbreaks benefit from such methods due to the increased coverage of disease in communities, which often go unreported. \tA scoping review was performed to describe the current state of research of the creation of syndromic surveillance systems for human infectious disease. A narrative synthesis built upon the scoping review to describe in detail all of the essential elements required in the creation of a syndromic surveillance system for human infectious disease. \tDescriptive analyses were performed on three sources of Ontario norovirus data spanning 2009 to 2014: positive samples submitted for laboratory testing, institutional outbreaks, and calls to telehealth with vomiting as a chief complaint. However, it is important to note that the calls to telehealth (syndromic data source) were only used as a proxy for norovirus. The gender and age distribution, institutions most commonly affected, total outbreak counts, total positive sample counts, and seasonality of norovirus were determined. Using this information to define the winter norovirus season and out-of-season periods, Shewhart (control) chart methods were employed to create the early warning syndromic surveillance system using the telehealth data as the source of syndromic data. These data were shown to predict the laboratory results by two weeks, and annual early alarm thresholds for the winter norovirus season were devised. \tThis thesis demonstrates the unique role of telehealth syndromic surveillance for norovirus in Ontario. Although these methods are widely used in other countries, they are not used in Ontario at the present time. The results from this study serve as a proposal for implementation and utilization in Ontario public health. In doing so, this syndromic surveillance system could strengthen current surveillance methods and help determine the true burden of norovirus in Ontario.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".