Blockchain Based Real-Time Contact Tracing – A Secure Way to Mitigate Highly Infectious Diseases
Bibliographic record
Abstract
Contact tracing is an effective, data driven infectious disease control strategy that involves identifying cases of active virus carriers and their contacts in restricting further disease transmission. Despite the effectiveness of this strategy, there are serious concerns regarding the privacy and security of data that are collected in this process as individuals give up control over those data. This study aims to provide some building blocks for developing a secured blockchain-based mobile application for contract tracing to strengthen the infectious disease mitigation approach. It also attempts to understand the contrasting perspective of different stakeholders involved in the data collection process through stakeholder survey and their willingness to share/store identity and health-related data in a blockchain-based app. Finally, we suggest a framework in developing an app to automate contract tracing in a private, secure, maintainable environment. This study helps create a strategic roadmap for developing a secured contact tracing platform to mitigate highly communicable diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".