HIV Digital Vaccine Strategy: Proposal for Applying Blockchain in Preventing the Spread of HIV
Bibliographic record
Abstract
BACKGROUND: The HIV epidemic imposes a heavy burden on societal development. Protection of susceptible populations is the most feasible method for eliminating the spread of HIV. In the absence of a biological vaccine, the definitive solution is enabling susceptible populations to recognize and avoid high-risk sexual behavior. OBJECTIVE: The objective of this study is to use specific technologies and strategies to establish a system by which high-HIV-risk individuals can determine the HIV infection status of one another anonymously, conveniently, and credibly. METHODS: This study proposes an HIV digital vaccine (HDV) strategy, a decentralized application (Dapp) based on blockchain for use by individuals with a high risk of HIV and accredited testing agencies (ATAs). Following testing, only the HIV-negative results (or linked information) are uploaded to the blockchain, which results in high-risk individuals being able to determine the HIV-negative status of each other anonymously, conveniently, and credibly. RESULTS: Future work includes the following: (1) a survey of the willingness to use Dapps among high-HIV-risk populations, (2) a larger framework containing both HDV and people living with HIV (PLH) and discussing the influence of HDV on PLH and its possible solutions, and (3) coordinating with the blockchain development team, ATAs, community-based organizations, and third-party organizations to raise funds, develop the Dapp, formulate detailed plans, and publicize and promote it. The exact timeline for achieving these objectives cannot be determined at present. CONCLUSIONS: The HDV strategy may reduce the occurrence of high-risk sexual behavior and effectively protect susceptible populations; combined with current strategies, it is a promising solution to prevent the spread of HIV. The included concepts of decentralized surveillance and surveillance as intervention may spark a change in current infectious disease prevention and control modes to introduce beneficial innovations in public health systems globally. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37133.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".