A Framework for Social Network-Based Dynamic Modeling and Prediction of Communicable Diseases
Bibliographic record
Abstract
It was published lately in 2016 that there are approximately 3.7 million of deaths caused by communicable diseases annually.Unfortunately, currently there is no automated method for the detection and tracking of communicable diseases progression.In this paper, a framework is proposed, that is based on social network analysis, different biological sensors, and big data analytics as for predicting and analyzing communicable disease and to facilitate the process of managing, preventing and predicting risks of communicable disease progression.The proposed framework is largely based on graph theory and social network analysis algorithms to model and dynamically predict communicable disease risk for diagnosed and non-diagnosed patients.In this research, a global graph structure that maps a whole friendship network is proposed, and the suitable algorithms to identify and continuously monitor a certain communicable disease progression rate.This research can potentially be useful for forming a methodology for early intervention and prevention policies targeted at patients that can potentially divert them from the disease pathway.The interpretation and dynamic utilities offered by the framework and its predictive capability are considered a remarkable and promising broad model highlighting potential pathways linking social support, biological sensors and data sciences to physical health.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".