Advancing Indoor Epidemiological Surveillance: Integrating Real-Time Object Detection and Spatial Analysis for Precise Contact Rate Analysis and Enhanced Public Health Strategies
Bibliographic record
Abstract
In response to escalating concerns about the indoor transmission of respiratory diseases, this study introduces a sophisticated software tool engineered to accurately determine contact rates among individuals in enclosed spaces-essential for public health surveillance and disease transmission mitigation. The tool applies YOLOv8, a cutting-edge deep learning model that enables precise individual detection and real-time tracking from video streams. An innovative feature of this system is its dynamic circular buffer zones, coupled with an advanced 2D projective transformation to accurately overlay video data coordinates onto a digital layout of the physical environment. By analyzing the overlap of these buffer zones and incorporating detailed heatmap visualizations, the software provides an in-depth quantification of contact instances and spatial contact patterns, marking an advancement over traditional contact tracing and contact counting methods. These enhancements not only improve the accuracy and speed of data analysis but also furnish public health officials with a comprehensive framework to develop more effective non-pharmaceutical infection control strategies. This research signifies a crucial evolution in epidemiological tools, transitioning from manual, simulation, and survey-based tracking methods to automated, real time, and precision-driven technologies that integrate advanced visual analytics to better understand and manage disease transmission in indoor settings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".