A Data Dashboard for Informed Healthcare Decisions
Bibliographic record
Abstract
In recent years, public health surveillance systems have played an important role in tracking health trends, detecting outbreaks early, and protecting communities. With a vast amount of available data, effective visualization tools can simplify the identification of patterns and trends, empowering healthcare specialists to make informed decisions. Our work focuses on developing a comprehensive pandemic surveillance dashboard tailored for healthcare specialists. The dashboard leverages multiple data sources, including wastewater surveillance data, public health data from the Public Health Ontario, Google News for recent news on respiratory diseases, and sentiment analysis of social media engagement on COVID-19 topics in Ontario. These diverse datasets provide a multifaceted perspective on the pandemic's progression. We implemented the dashboard using Power BI to automate data visualization and enable users to explore trends through interactive charts and real-time updates. This tool aims to assist healthcare specialists in monitoring outbreaks, evaluating the impact of interventions, and predicting potential surges in cases. Future studies will focus on correlating trends observed across datasets, such as linking wastewater viral loads with public health metrics and analyzing sentiment analysis data for early outbreak indicators. These efforts aim to further enhance the dashboard's predictive capabilities and its role in pandemic preparedness. By integrating data from multiple sources and presenting them in an intuitive format, this dashboard serves as a vital tool for public health professionals in their ongoing fight against respiratory pandemics.
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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 | 0.022 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".